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• Newbie study using standard ML techniques with HAR Application and Discussions. • Activities found in Literature with the corresponding reference. • Co-occurrences between activities and sensors with the corresponding reference. • Summary and comparison among the different datasets found in Literature. • Summary of the experimentation settings with performance scores found in Literature. Human Activity Recognition (HAR) is an essential area of research related to the ability of smartphones to retrieve information through embedded sensors and recognize the activity that humans are performing. Researchers have recognized people's activities by processing the data received from the sensors with Machine Learning Models. This work is intended to be a hands-on survey with practical's tables capable of guiding the reader through the sensors used in modern smartphones and highly cited developed machine learning models that perform human activity recognition. Several papers in the literature have been studied, paying attention to the preprocessing, feature extraction, feature selection, and classification techniques of the HAR system. In addition, several summary tables illustrating HAR approaches have been provided: most popular human activities in the literature with paper references, the most popular datasets available for download (Analyzing their characteristics, such as the number of subjects involved, the activities recorded, and the sensors with online-availability), co-occurrences between activities and sensors, and a summary table showing the performance obtained by researchers. =The paper's goal is to recommend, through the discussion phase and thanks to the tables, the current state of the art on this topic. [ABSTRACT FROM AUTHOR]

Roundwood sorting is still a manual process in many Swiss sawmills, requiring employees to visually inspect and categorize thousands of logs per day. The heavy workload can be both physically and mentally taxing and can lead to increased rates of human error. State-of-the-art automation systems like X-ray log scanners are expensive and difficult to integrate into existing process lines. This paper proposes a novel recommendation system that leverages recent advances in image classification to automate roundwood classification by quality and species. The system integrates a camera to capture cross-sectional images of logs and record numerical data, such as length, taper, and diameter. The analysis of the resulting dataset highlights the challenges of data imbalance and noise, which makes classification difficult and, in some cases, impossible. However, by using selected datasets with reduced noise, state-of-the-art Convolutional Neural Networks (CNNs) can extract quality and species features. Quality models learn from a manually selected and simplified dataset, featuring samples that experts can clearly classify based on the image's information. Species models are trained on a label-noise-reduced dataset, reflecting real-world complexity. The accuracy on the selected dataset for three quality classes is 80%. The species determination is less challenging and reaches 91% accuracy on a synchronized dataset for the main species spruce and fir. Overall, this paper highlights the potential of Machine Learning in augmenting the roundwood sorting processes and presents a novel system that can improve the efficiency and accuracy of the process. [Display omitted] • Automation of roundwood sorting: Replaces manual sorting with image-based AI. • Integrated camera system in roundwood sorting to collect labeled dataset. • Species prediction: 91% accuracy in spruce–fir distinction. • Quality prediction on complexity reduced dataset: 80% accuracy between three main quality levels. • Efficient, adaptable & scalable system which is easy to integrate into existing process lines. [ABSTRACT FROM AUTHOR]

• A feature selection method based on binary dandelion algorithm is proposed. • The algorithm applies a sine-cosine operator and a restart strategy. • Mutual information and quick bit mutation improve the performance of the algorithm. • The algorithm performs well on datasets with larger dimensions. Feature selection (FS) is an important data preprocessing technology for machine learning and data mining. Metaheuristic algorithm (MH) has been widely used in feature selection because of its powerful search function. This paper presents an improved Binary Dandelion Algorithm using Sine Cosine operator and Restart strategy (SCRBDA) for feature selection. First, the sine cosine operator is used in the radius formula of the core dandelions (CD), which significantly enhances the ability of algorithm development and exploration. Secondly, the algorithm uses a restart strategy to increase its ability to get rid of local optimum. Thirdly, mutual information is used to guide the generation of some dandelions, which pays more attention to the correlation between the selected features and categories. Finally, quick bit mutation is used as the mutation strategy to improve the diversity of the population. The SCRBDA proposed in this paper was tested on 18 datasets of different sizes from UCI machine learning database. The SCRBDA was compared with 8 other classical feature selection algorithms, and the performance of the proposed algorithm was evaluated through feature subset size, classification accuracy, fitness value, and F1-score. The experimental results show that SCRBDA achieves the best performance, which has stronger feature reduction ability and achieves better overall performance on most datasets. Especially on large-scale datasets, SCRBDA can obtain extremely smaller feature subsets while maintaining much higher classification accuracy, and satisfactory F1-score. [ABSTRACT FROM AUTHOR]

In this paper the performance of different Machine Learning and Deep Learning approaches is evaluated in problems related to green mobility in big cities. Specifically, the forecasting of bike sharing demand in Madrid and Barcelona (Spain) is approached, for different prediction time-horizons, and also a problem of cable car demand forecasting in Madrid city. An important number of predictive variables are considered, which are grouped into four different sets (categorical/calendrical, persistence-based, meteorological and, as a novelty of the paper, information about analogue past instances), whose relevance is studied for all cases. A feature selection mechanism is also incorporated in order to improve the prediction accuracy of the proposed algorithms. A total of 12 different multivariate regression techniques are implemented, covering from Machine Learning methods to time-series Deep Learning approaches. Excellent results in all the prediction problems approached are reported. Finally, the consequences of obtaining accurate prediction in these three problem of green mobility in big cities are discussed. In addition, it is studied how the results could be exported to other similar cases in more general urban mobility studies. Novelties of the work include: (1) Addressing the forecast problem of passenger flow on a cable car using ML and DL multivariate techniques; (2) using the demand of analogous past instances as an additional feature to solve the demand prediction problems; and (3) the extraction of global conclusions about feature relevance when addressing a demand forecasting problem in green mobility. [ABSTRACT FROM AUTHOR]

• Pinch force strength is accurately predicted using sEMG. • Different pinch movements are accurately classified using sEMG. • Similarity of EMG features is evaluated. • An EMG feature-to-image method is proposed. The movement of intelligent EMG-driven prosthesis mainly relies on the synergy of different fingers to achieve function of grasping objects. The paper proposes a novel scheme for force prediction and movement classification about pinch between different fingers based on surface electromyography (sEMG) using machine learning. The pinch force and sEMG signals are recorded synchronously by a data acquisition device. Eight features are extracted, which are proven to have better performance in the estimation of sEMG-to-force. We present a novel feature selection method that uses the one-dimensional time series similarity assessment based on Manhattan distance to eliminate the repetitive information between features. Three optimal features carrying less repetitive information are retained. Seven machine learning algorithms are used to predict force strength. The results show that the Long Short Term Memory (LSTM) has the best performance of force prediction, achieving a R 2 of 0.9517 and RMSE of 3.2723. The paper proposes a novel method of converting EMG feature sequences to the normalized gray image in order to classify the finger movement. Five classifiers based on image feature extraction and the Convolutional Neural Network (CNN) are developed respectively. The experimental results indicate that the CNN performs best, achieving an accuracy of 97.66%. In this way, it not only realizes the accurate force prediction, but also realizes the movement classification between different fingers. The proposed methodology has the potential to realize simultaneous force and movement control of prosthetic hand. [ABSTRACT FROM AUTHOR]

Feature selection refers to the process of identifying the most relevant and valuable features or characteristics in a dataset, and it is an important step that can improve the performance and accuracy of predictive models for machine learning and data mining. Feature selection is crucial in efficiently reducing the number of features, thereby enhancing classification accuracy. This, in turn, alleviates computational burdens and elevates the overall performance of machine learning algorithms. This paper extended the conventional manta ray foraging optimization (MRFO) algorithm to an improved binary MRFO (IBMFO) algorithm. IBMRFO serves as a search strategy for designing a wrapper-based feature selection approach. To commence, we introduce a novel mechanism, termed the chaotic tent map (CTM), aimed at initializing the population to fortify both the algorithm's exploitation capabilities and population diversity. Second, an adaptive somersault foraging (ASF) factor is tuned to avoid premature convergence. Finally, a binary mechanism is introduced to adapt the algorithm for binary feature selection dilemmas. Experiments are performed on 30 renowned UC Irvine machine learning repository datasets. The results of the student's t-test show that the classification algorithm has statistical significance. The findings unequivocally exhibit the superior performance of the proposed IBMRFO compared to other algorithms under scrutiny. Notably, the efficiency of the enhanced factors is assessed through rigorous testing. For post-publications supporting this work, readers can refer to https://github.com/ELF-CHEUNG/IBMRFO. • Novel chaotic tent map for population initialization from evolutionary computation. • Optimization methods based on enhanced evolutionary computation. • Adaptive factor enhances convergence rate and search efficiency of algorithm. • Extensive numerical and scientific simulations for verifications with UCI dataset. [ABSTRACT FROM AUTHOR]

Feature selection has a prominent role in high-dimensional datasets to increase classification accuracy, decrease the learning algorithm computational time, and present the most informative features to decision-makers. This paper proposes a two-stage hybrid feature selection for high-dimensional medical datasets: Maximum Pattern Recognition - Multi-objective Discrete Evolution Strategy (MPR-MDES). MPR is a rapid filter ranker that significantly outperforms existing frequency-based rankers in recognizing non-linear patterns, effectively eliminating a majority of non-informative features. Then, the wrapper Multi-objective Discrete Evolution Strategy (MDES) uses the remaining features and obtains sets of solutions which are automatically presented to decision-makers. The experiments conducted on large medical datasets demonstrate that MPR-MDES achieves considerable improvements compared to state-of-the-art methods, in terms of both classification accuracy and dimensionality reduction. In this sense, the proposal successfully performs when presenting informative feature sets to decision-makers. The implementation is available on https://github.com/KhaosResearch/MPR-MDES. [Display omitted] • Maximum Pattern Recognition (MPR) is proposed as a frequency-based filter ranker. • Multi-objective Discrete Evolution Strategy (MDES) is proposed as a wrapper method. • The proposed MPR-MDES selects the best feature sets in the Pareto front. [ABSTRACT FROM AUTHOR]

Highlights • Fault prediction improves the effectiveness of software quality assurance activities. • This paper focuses on building an effective fault prediction classifier. • Fault prediction model using Iterated feature selection algorithms with L-RNN. • We perform experiments on 19 open source projects. • Fault prediction model is best suitable for projects with faulty classes less than the threshold value. Abstract Software fault prediction (SFP) is typically used to predict faults in software components. Machine learning techniques (e.g., classification) are widely used to tackle this problem. With the availability of the huge amount of data that can be obtained from mining software historical repositories, it becomes possible to have some features (metrics) that are not correlated with the faults, which consequently mislead the learning algorithm and thus decrease its performance. One possible solution to eliminate those metrics is Feature Selection (FS). In this paper, a novel FS approach is proposed to enhance the performance of a layered recurrent neural network (L-RNN), which is used as a classification technique for the SFP problem. Three different wrapper FS algorithms (i.e, Binary Genetic Algorithm (BGA), Binary Particle Swarm Optimization (BPSO), and Binary Ant Colony Optimization (BACO)) were employed iteratively. To assess the performance of the proposed approach, 19 real-world software projects from PROMISE repository are investigated and the experimental results are discussed. Receiver operating characteristic - area under the curve (ROC-AUC) is used as a performance measure. The results are compared with other state-of-art approaches including Naïve Bayes (NB), Artificial Neural Network (ANN), logistic regression (LR), the k-nearest neighbors (k-NN) and C4.5 decision trees, in terms of area under the curve (AUC). Our results have demonstrated that the proposed approach can outperform other existing methods. [ABSTRACT FROM AUTHOR]

Background —Nowadays, with the rise of Internet access and mobile devices around the globe, more people are using social networks for collaboration and receiving real-time information. Twitter, the microblogging site that is becoming a critical source of communication, has also grabbed the attention of spammers to distract users. So far, researchers have introduced various defense techniques to detect spams and combat spammers' activities. To overcome this problem, many novel techniques have been offered by researchers, which have greatly enhanced spam detection performance. Objective —The purpose of this paper is to identify, taxonomically classify, and compare current Twitter spam detection approaches in a systematic way. Method —This study presents a comprehensive Systematic Literature Review (SLR) method for spam detection on Twitter regarding 70 most relevant papers published between 2010 and October 2022. Literature review analysis reveals that most of the existing Twitter spam detection techniques are based on textual content and messages (tweets) that rely on Machine Learning (ML)-based algorithms. The major differences in these ML algorithms which use various classification and clustering algorithms are related to various feature selection methods. Hence, we propose a classification based on different feature selection analyses, namely content analysis, user analysis, tweet analysis, network analysis, and hybrid analysis. Results —Various parameters are identified to investigate the Twitter spam detection approaches, and each of the papers was examined to find the research methodology and present comparative studies on current approaches. Conclusion —This paper demonstrates that the existing Twitter spam detection approaches have encountered several open issues, including scalability, streaming data analysis, and processing. The most obvious unresolved issues are spam drift and non-English tweets. [ABSTRACT FROM AUTHOR]

The conventional standardized theoretical models (such as Webster, Alcelik, Indo-HCM) used for the delay estimation revolve around the mathematical hypothesis and assumptions, making them static with limitations in accommodating the dynamic traffic behaviors. Recent advances in artificial intelligence make machine learning techniques suitable for estimating vehicle control delay compared to conventional methods. This paper demonstrates the application of several machine learning models developed by focusing on the fluctuations of traffic observed at an intersection having heterogeneous traffic conditions in Ahmedabad city for delay estimation. Several parameters are extracted from on-field and video surveys. However, not all parameters are relevant for accurately estimating vehicle control delay. Hence, a feature selection process consisting of several feature-scoring techniques from filter, wrapper, and embedded methods is applied to all the parameters. This process gave insights into the most statistically relevant independent parameters, and out of all the parameters, cycle time was found to be insignificant, with a feature score of 0 from almost all techniques. Hence, it was removed, and then the prominent parameters were then used to build a vehicle control delay model using support vector regression (SVR), K-nearest neighbor (KNN), artificial neural network (ANN), random forest regression (RF), and decision tree regression (DT). Error distribution, standard deviation of errors, coefficient of Determination (R2), and Root Mean Squared Error (RMSE) are the parameters used for evaluating the performance of the machine learning models. RF outperformed them all with a standard deviation of errors, R2, and RMSE of 11.065, 0.926, and 11.081 on testing data. But ANN, KNN, and DT also performed satisfactorily. Compared with conventional standardized theoretical models, all the machine learning models except SVR performed better. [ABSTRACT FROM AUTHOR]

• MMODE is developed as a new hybrid gene selection method for tumor identification. • MMODE combines multi-tasking and multi-objective frameworks. • MMODE uses a new elite strategy and a new guidance strategy. • MMODE selects a few genes and achieves high classification accuracy. A key preprocessing step in tumor recognition based on microarray expression profile data and machine learning is to identify tumor marker genes. Gene selection aims to select the most relevant gene subset from the original ultra-high dimensional microarray expression profile data to improve tumor identification performance. Inspired by evolutionary multitasking (EMT) and multi-objective optimization, this paper puts forward a novel multitasking multi-objective differential evolution gene selection algorithm (MMODE) which uses new elite and guidance strategies to select the best gene subsets. MMODE initializes two different populations according to different filtering criteria to increase the diversity of the search. These two populations guide their respective populations to search in the optimal direction through knowledge transfer in the evolutionary process. In addition, MMODE employs new elite and guidance strategies that enables individuals to narrow the search range and jump out of local optima. The proposed algorithm is validated on 13 publicly available microarray expression datasets in comparison with state-of-the-art gene selection algorithms. The experimental results show that MMODE can find smaller gene subsets and achieve higher classification accuracy compared with other algorithms. [ABSTRACT FROM AUTHOR]

The high dimensionality of data hinders the learning ability of machine learning algorithms. Feature selection techniques can be used to reduce dimensionality, which is an important step for processing high-dimensional data. Feature selection solves this problem by removing irrelevant and redundant information, which can improve learning models, reduce calculation time, and improve learning accuracy. In this paper, a novel filter in mixed-attribute datasets for feature selection is proposed. The independent attributes are mixed or heterogeneous in the sense that both numerical and categorical attribute types may appear together in the same dataset. Based on the preordonnances theory, we use a new concept to quantify the relevance and redundancy of features even if there are heterogeneous (mixed-type) data. The technique for order preference by similarity to the ideal solution is one of the well-known multicriteria decision-making methods; it is utilized as a weighting and informative feature selection filter. To assess the effectiveness of the proposed method, several experiments, both simulated and real, are performed, including a comparison to other well-known filter methods. The experimental results show that, in most cases, the method yielded competitive results in comparison to other methods. • Introducing new criterions for relevance and complementarity. • Dealing with mixed-attribute datasets without requiring a preprocessing step. • The multi-criteria decision method, namely TOPSIS, is used to score the explanatory features. • Investigate the performance of the proposed filter in high-dimensional data by using simulated and real data sets. [ABSTRACT FROM AUTHOR]

Heart rate variability serves as a valuable indicator and biomarker for stress detection and monitoring. Feature selection, which aims to identify relevant features from a large set of variables, is a crucial preprocessing step towards this. However, this task becomes challenging due to high dimensionality and the presence of irrelevant and redundant attributes. The Minimum Redundancy and Maximum Relevance (mRMR) feature selection method addresses this challenge by selecting relevant features while controlling redundancy. This paper presents extensions and evaluated versions of the mRMR feature selection methods for stress detection using Heart Rate Variability (HRV) measures. The proposed feature selection methods extend the traditional mRMR by replacing the Pearson correlation redundancy with non-linear feature redundancy measures capable of capturing more complex relationships between variables. An extensive empirical evaluation is conducted on the proposed mRMR extensions, comparing them with four other baseline feature selection methods using three publicly available datasets. The experimental results demonstrate the effectiveness of incorporating the non-linear feature redundancy measure into the feature selection process. [ABSTRACT FROM AUTHOR]

Accurate and timely diagnosis of Type 2 Diabetes is a highly challenging task due to its initial asymptomatic nature and complex risk factor composition. Recently, Machine Learning (ML) has been actively used to build improved Type 2 Diabetes classification systems. One important aspect of these systems has been feature selection. Filter feature selection techniques especially based on mutual information have been popularly employed in recent works. However, most of them have focused on selecting relevant features and eliminating redundant ones. A third relationship called feature interaction may exist if input features are highly correlated, as in the case of Type 2 Diabetes. Feature interaction signifies the additional information about the target provided by the interaction between a subset of input features, that may not be relevant to the target individually. Second, many of the existing ML models are black-box, making the model interpretability very difficult. This paper proposes an efficient ML framework for the classification of Prediabetes and Type 2 Diabetes by incorporating feature interactions. It presents a hybrid filter-wrapper technique called Feature Interaction-based Greedy Sequential Feature selection. Agglomerative Feature Clustering and Dendrogram visualization for the analysis of interactive features is performed. A model-agnostic explainability technique of SHapley Additive explanations (SHAP) is augmented to provide local and global explanations of model predictions. The performance of the proposed classification framework was found to be interpretable as well as efficient with an accuracy of 98.8669%, precision of 98.8660%, recall of 98.8665%, and F-score of 0.9886 for Diabetes. For Prediabetes, an accuracy of 90.1187%, precision of 94.6958%, recall of 90.1187%, and F-score of 0.9403 was obtained. [ABSTRACT FROM AUTHOR]

• AER system utilizes kinematic data for enhanced human–computer interaction. • Diverse algorithms achieve >99% success (small windows), >88% (larger). • Innovative analysis: joint neighborhood, window size, axis combination efficiency. • Practical CNN algorithms enable real-time emotion recognition. • Insights from transparent datasets lead to emotion recognition advancements. This study introduces an automatic emotion recognition system (AER) focusing on skeletal-based kinematic datasets for enhanced human–computer interaction. Departing from conventional approaches, it achieves real-time emotion recognition in real-life situations. The dataset covers seven emotions and undergoes assessment by eight diverse machine and deep learning algorithms. A thorough investigation is undertaken by varying window sizes and data states, including raw positions and feature-extracted data. The findings imply that incorporating advanced techniques like joint-related feature extraction and robust classifier models yields promising outcomes. Dataset augmentation via varying window sizes enriches insights into real-world scenarios. Evaluations exhibit classification accuracy surpassing 99% for small windows, 94% for medium, and exceeding 88% for larger windows, thereby confirming the robust nature of the approach. Furthermore, we highlight window size's impact on emotion detection and the benefits of combining coordinate axes for efficiency and accuracy. The analysis intricately examines the contributions of features at both the joint and axis levels, assisting in making well-informed selections. The study's contributions include carefully curated datasets, transparent code, and models, all of which ensure the possibility of replication. The paper establishes a benchmark that bridges theory and practicality, solidifying the proposed approach's effectiveness in balancing accuracy and efficiency. By pioneering advanced AER through kinematic data, it sets a new standard for efficacy while driving seamless human–computer interaction through rigorous analysis and strategic design. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has made its way into many disciplines ranging from health care to self-driving cars. In financial markets, we see a rich literature for DL applications. Particularly, investors require robust algorithms that can navigate and make sense of extremely noisy and volatile markets. In this work, we use deep learning to select a portfolio of stocks and use a genetic algorithm to optimize the hyperparameters of DL. The work analyzes the improvement in using genetic-based hyperparameter optimization over grid searches. The Genetic Algorithm brings 40% improvements in prediction when compared to a random-grid search. Novelty-wise, the work couples a genetic-based hyperparameter optimization with multiple Deep RankNet models to predict the behavior of financial assets. Our results show promising portfolio returns 20% better than the general market. In the highly volatile COVID 19 period, the models exceed market returns by more than double. Overall, this paper brings a comprehensive work that integrates hyperparameter optimization, Deep RankNet, LSTM, period size variations, input variable transformation, feature selection, training/evaluation ratio analysis, and multiple portfolio selection strategies. [ABSTRACT FROM AUTHOR]

Vertical federated learning (VFL) is a privacy preserving collaborative machine learning technique designed for distributed learning scenarios in which data from different parties have overlap in the sample space. In this paper, a VFL method for feature selection, which is an effective dimensionality reduction technique that selects a subset of informative features from high-dimensional data by eliminating irrelevant and redundant features, is proposed. Because of the potential insufficiency of useful information for learning informative features and the difficulty in sharing raw data among parties due to the increasing awareness of data privacy protection, it is desirable to exploit information from multiple parties without raw data sharing. In this paper, we propose a VFL-based feature selection method that leverages deep learning models as well as complementary information from features in the same samples at multiple parties without data disclosure. In order to further improve feature selection performance, information of samples that do not have features appearing in all parties are also utilized. Promising results in extensive experiments show the effectiveness of the proposed approach in terms of collaborative feature selection without data sharing. • In this paper, we bridge this gap by proposing a novel VFL-based feature selection method—Vertical Federated Learning-based Feature Selection (VFLFS). To the best of our knowledge, this is the first deep learning-based vertical federated learning approach with feature selection. • A strategy to make use of non-overlapping samples is also proposed to improve feature selection effectiveness. • The proposed VFLFS approach has been evaluated extensively based on real-world datasets. The results show that VFLFS can significantly improve model performance under VFL settings compared to four state of the art baselines, especially in conditions where a large proportion of the data samples do not overlap across data owners. [ABSTRACT FROM AUTHOR]

Due to the presence of redundant and irrelevant features in large-dimensional biomedical datasets, the prediction accuracy of disease diagnosis can often be decreased. Therefore, it is important to adopt feature extraction methodologies that can deal with problem structures and identify underlying data patterns. In this paper, we propose a novel approach called the Anti Coronavirus Optimized Kernel-based Softplus Extreme Learning Machine (ACO-KSELM) to accurately predict different types of skin cancer by analyzing high-dimensional datasets. To evaluate the proposed ACO-KSELM method, we used four different skin cancer image datasets: ISIC 2016, ACS, HAM10000, and PAD-UFES-20. These dermoscopic image datasets were preprocessed using Gaussian filters to remove noise and artifacts, and relevant features based on color, texture, and shape were extracted using color histogram, Haralick texture, and Hu moment extraction approaches, respectively. Finally, the proposed ACO-KSELM method accurately predicted and classified the extracted features into Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC), Actinic Keratosis (ACK), Seborrheic Keratosis (SEK), Bowen's disease (BOD), Melanoma (MEL), and Nevus (NEV) categories. The analytical results showed that the proposed method achieved a higher rate of prediction accuracy of about 98.9%, 98.7%, 98.6%, and 97.9% for the ISIC 2016, ACS, HAM10000, and PAD-UFES-20 datasets, respectively. [ABSTRACT FROM AUTHOR]

Multi-dimensional classification (MDC) task can be considered the most inclusive description of all classifications tasks, as it joins multiple class spaces and their multiple class members into a single compound classification problem. The challenges in MDC arise from the possible class dependencies across different class spaces, as well as the imbalance of labels in training datasets due to lack of all possible combinations. In this paper, we propose a straightforward, yet efficient, MDC deep learning classifier, named Deep Self-Organizing Cube (DSOC) that can model dependencies among classes in multiple class spaces, while consolidating its ability to classify rare combinations of labels. DSOC is formed of two n -dimensional components, namely the Hypercube Classifier and the multiple DSOC Neural Networks connected to the hypercube. The multiple neural networks component is responsible for feature selection and segregation of classes, while the Hypercube classifier is responsible for creating the semantics among multiple class spaces and accommodate the model for rare sample classification. DSOC is a multiple-output learning algorithm that successfully classify samples across all class spaces simultaneously. To challenge the proposed DSOC model, we conducted an assessment on seventeen benchmark datasets in the four types of classification tasks, binary, multi-class, multi-label and multi-dimensional. The obtained results were compared to four standard classifiers and eight competitive state-of-the-art approaches reported in literature. The DSOC has achieved superior performance over standard classifiers as well as the state-of-the-art approaches in all the four classification tasks. Moreover, in terms of Exact Match accuracy metrics, DSOC has outperformed all state-of-the-art approaches in 77.8% of the cases, which reflects the superior ability of DSOC to model dependencies and successfully classify rare samples across all dimensions simultaneously. [ABSTRACT FROM AUTHOR]

Highlights • Feature selection over high-dimensional colon cancer Microarray Datasets. • Features selected from both Gene Expression and DNA-Methylation Microarray datasets. • Resultant six biomarker genes for colon cancer validated using Enrichment Analysis. • Biomarker genes validated on independent datasets with 99.9% classification accuracy. Abstract Cancer is a dangerous disease that causes death worldwide. Discovering few genes relevant to one cancer disease can result in effective treatments. The challenge associated with the Microarray datasets is its high dimensionality; the huge number of features compared to the modest number of samples in these datasets. Recent research efforts attempted to reduce this high-dimensionality using different feature selection techniques. This paper presents an ensemble feature selection technique based on t -test and genetic algorithm. After preprocessing the data using t -test, a Nested Genetic Algorithm, namely Nested-GA, is used to get the optimal subset of features by combining data from two different datasets. Nested-GA consists of two Nested Genetic Algorithms (outer and inner) that run on two different kinds of datasets. The Outer Genetic Algorithm (OGA-SVM) works on Microarray gene expression datasets, whereas the Inner Genetic Algorithm (IGA-NNW) runs on DNA Methylation datasets. Nested-GA is performed on a colon cancer dataset with 5-fold cross validation. After applying Nested-GA, the Incremental Feature Selection (IFS) strategy is used to get the smallest optimal genes subset. The genes subset has been validated on an independent dataset resulting in 99.9% classification accuracy. Consequently, the biological significance of the resulting optimal genes is validated using Enrichment Analysis. Moreover, the results of Nested-GA have been compared to the results of other feature selection algorithms that have been run on either Gene Expression or DNA Methylation datasets. From the experimental results, Nested-GA showed the highest classification performance with a small optimal feature subset compared to the other algorithms. Furthermore, by running Nested-GA on lung cancer datasets that contain two different cancer subtypes, it resulted in significantly better classification accuracy (98.4%) compared to the accuracy of a previous research (84.6%) that utilized lung cancer DNA-Methylation data only. [ABSTRACT FROM AUTHOR]

Highlights • Two binary variants of the butterfly optimization algorithm (BOA) are proposed. • Two transfer functions are used to map the continuous search space to discrete one. • Twenty one UCI datasets are utilized in the experiments. • A superior performance of the proposed binary variants is proved in the experiments. Abstract In this paper, binary variants of the Butterfly Optimization Algorithm (BOA) are proposed and used to select the optimal feature subset for classification purposes in a wrapper-mode. BOA is a recently proposed algorithm that has not been systematically applied to feature selection problems yet. BOA can efficiently explore the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The two proposed binary variants of BOA are applied to select the optimal feature combination that maximizes classification accuracy while minimizing the number of selected features. In these variants, the native BOA is utilized while its continuous steps are bounded in a threshold using a suitable threshold function after squashing them. The proposed binary algorithms are compared with five state-of-the-art approaches and four latest high performing optimization algorithms. A number of assessment indicators are utilized to properly assess and compare the performance of these algorithms over 21 datasets from the UCI repository. The experimental results confirm the efficiency of the proposed approaches in improving the classification accuracy compared to other wrapper-based algorithms, which proves the ability of BOA algorithm in searching the feature space and selecting the most informative attributes for classification tasks. [ABSTRACT FROM AUTHOR]

Background: Classification and localization of focal epileptic seizures provide a proper diagnostic procedure for epilepsy patients. Visual identification of seizure activity from long-term electroencephalography (EEG) is tedious, time-consuming and leads to human error. Therefore, there is a need for an automated classification system. Methods: In this paper, we introduce a tool called CADFES: computerized automated detection of focal epileptic seizures. For the study, total 41.66 hours of EEG data from the Bern-Barcelona database was used. Set of 28 features were extracted from time, frequency, and statistical domain and significant features were selected using neighborhood component analysis (NCA). In NCA, optimization of regularization parameter ensured better classification accuracy (less classification loss) with seven features. The performance of the algorithm was assessed using support vector machine (SVM), K-nearest neighbor (K-NN), random forest and adaptive boosting (AdaBoost) classifiers. Results: Experimental results revealed sensitivity, specificity, accuracy, positive predictive rate, negative predictive rate, and area under the curve of 97.6%, 94.4%, 96.1%, 92.9%, 98.8% and 0.96 respectively using the SVM classifier. Finally, MATLAB based software tool referred to as CADFES was introduced for automated classification of focal and non-focal seizures. Comparison results ensure that proposed study is superior to existing methods. Hence, it is expected to perform better at the hospitals for automated classification of focal epileptic seizures in real-time. [ABSTRACT FROM AUTHOR]

In recent years, cyber-attacks against businesses and organizations have increased. Such attacks usually result in significant damage to the organization, such as the loss and/or leakage of sensitive and confidential information. Because email communication is an integral part of daily business operations, attackers frequently leverage email as an attack vector in order to initially penetrate the targeted organization. Email message allows the attacker to deliver dangerous content to the victim, such as malicious attachments or links to malicious websites. Existing email analysis solutions analyze only specific parts of the email using rule-based methods, while other important parts remain unanalyzed. Existing anti-virus engines primarily use signature-based detection methods, and therefore are insufficient for detecting new unknown malicious emails. Machine learning methods have been shown to be effective at detecting maliciousness in various domains and particularly in email. Previous works which used machine learning methods suggested sets of features which offer a limited perspective over the whole email message. In this paper, we propose a novel set of general descriptive features extracted from all email components (header, body, and attachments) for enhanced detection of malicious emails using machine learning methods. The proposed features are extracted just from the email itself; therefore, our features are independent, since the extraction process does not require an Internet connection or the use of external services or other tools, thereby meeting the needs of real-time detection systems. We conducted an extensive evaluation of our new novel features against sets of features suggested by previous academic work using a collection of 33,142 emails which contains 38.73% malicious and 61.27% benign emails. The results show that malicious emails can be detected effectively when using our novel features with machine learning algorithms. Moreover, our novel features enhance the detection of malicious emails when used in conjunction with features suggested by related work. The Random Forest classifier achieved the highest detection rates, with an AUC of 0.929, true positive rate (TPR) of 0.947, and false positive rate (FPR) of 0.03. We also present the IDR (integrated detection rate), a new measure which helps calibrate the threshold of a machine learning classifier in order to achieve the optimal TP and FP rates, which are the most important measures for a real-time and practical cyber-security application. [ABSTRACT FROM AUTHOR]

Large amounts of information and various features are in many machine learning applications available, or easily obtainable. However, their quality is potentially low and greater volumes of information are not always beneficial for machine learning, for instance, when not all available features in a data set are relevant for the classification task and for understanding the studied phenomenon. Feature selection aims at determining a subset of features that represents the data well, gives accurate classification results and reduces the impact of noise on the classification performance. In this paper, we propose a filter feature ranking method for feature selection based on fuzzy similarity and entropy measures (FSAE), which is an adaptation of the idea used for the wrapper function by Luukka (2011) and has an additional scaling factor. The scaling factor to the feature and class-specific entropy values that is implemented, accounts for the distance between the ideal vectors for each class. Moreover, a wrapper version of the FSAE with a similarity classifier is presented as well. The feature selection method is tested on five medical data sets: dermatology, chronic kidney disease, breast cancer, diabetic retinopathy and horse colic. The wrapper version of FSAE is compared to the wrapper introduced by Luukka (2011) and shows at least as accurate results with often considerably fewer features. In the comparison with ReliefF, Laplacian score, Fisher score and the filter version of Luukka (2011), the FSAE filter in general achieves competitive mean accuracies and results for one medical data set, the breast cancer Wisconsin data set, together with the Laplacian score in the best results over all possible feature removals. [ABSTRACT FROM AUTHOR]

In this paper, the focus is on the application of prototype selection to multi-label data sets as a preliminary stage in the learning process. There are two general strategies when designing Machine Learning algorithms that are capable of dealing with multi-label problems: data transformation and method adaptation. These strategies have been successfully applied in obtaining classifiers and regressors for multi-label learning. Here we investigate the feasibility of data transformation in obtaining prototype selection algorithms for multi-label data sets from three prototype selection algorithms for single-label. The data transformation methods used were: binary relevance, dependent binary relevance, label powerset, and random k -labelsets. The general conclusion is that the methods of prototype selection obtained using data transformation are not better than those obtained through method adaptation. Moreover, prototype selection algorithms designed for multi-label do not do an entirely satisfactory job, because, although they reduce the size of the data set, without affecting significantly the accuracy, the classifier trained with the reduced data set does not improve the accuracy of the classifier when it is trained with the whole data set. [ABSTRACT FROM AUTHOR]

Secure communications for sensitive information over the internet is a very crucial issue. Various malicious information is integrated with the real-time traffic that not only reduces the credibility of the transmission quality but also degrades the system performance. A network intrusion detection system is a tool that minutely inspects all the incoming and outgoing packets passing through a network and identifies malicious events. Although a moderate number of research work has been carried out for the development of network intrusion detection systems, few of them have concentrated on identifying attacks in real-time. Also, there is scope to enhance the performance of the system. Real-time attack identification not only needs a significant number of optimal features but also requires a set of high-performance predictive models. To address the aforementioned challenges, this paper proposes a bi-phase network intrusion detection system by judiciously inspecting the performance of various machine learning and ensemble learning frameworks. The real-time model has been constructed with the help of the CICIDS2017 dataset, which contains many modern-day attacks. A proposed Threshold-Correlation algorithm combined with Particle Swarm Optimization and Genetic algorithms has been used for relevant feature selection, and based on the selected features; the bi-phase model has been developed. The experimental results reveal that the chosen model achieves 99.82% and 99.41% detection accuracy in phase-1 and phase-2 respectively. Finally, the proposed framework has been evaluated using a custom-built real-time test-bed model. In this implementation, instantaneous network traffic (malicious and normal) has been generated by a separate system connected through a network, and on the detector side, the same has been captured and classified by the proposed model. Exhaustive performance evaluation in a real-time environment confirms both the effectiveness and superiority of the present work. [ABSTRACT FROM AUTHOR]

The application of Software Fault Prediction (SFP) in the software development life cycle to predict the faulty class at the early stage has piqued the interest of various scholars. In the SFP domain, during research analysis, it got realized that there has been very little work instigated on addressing both class imbalance and feature redundancy problems jointly to enhance the performance and prediction accuracy of SFP models. It has been perceived in the literature survey the study of droughts with the comprehensive comparative analysis of different sampling and feature selection strategies together. This research builds an extensive assessment of distinct combinations of different feature selection and sampling approaches, to effectively overcome the problems of class overlap, class imbalance, and feature redundancy. The objective is to determine the best combination that will produce results with a higher degree of accuracy and an effective SFP model. Considering the above erudition, the study has applied 8 different sampling techniques along with 10 feature selection algorithms against 56 open-source projects. The comparative analysis is performed against 5346 variants of input datasets by applying 8 different classifiers to predict the faulty class. In addition, the research paper presents an intensive assessment and performance of these techniques individually against all the input projects. We have considered accuracy and Area Under the ROC (receiver operating characteristic curve) Curve (AUC) performance metrics to compare the performance of different models developed using the classification algorithm. For each project in the proposed work, we evaluated a total of 792 combinations that were produced using 10 feature selection methods, 1 all metrics dataset, 8 sampling methods, 1 original, unsampled dataset, and 8 classifiers. The empirical result indicates that, against 21 projects out of 54 projects, Synthetic Minority Over Sampling Technique Edited (SMOTEE) with correlation-based feature selection (FS2) combination outperformed with the highest AUC value which is 38.89 % of projects. Additionally, according to experimental results, the highest AUC values were attained by 24.07 % of projects using the SMOTEE, FS2, and RF combination. The results of the statical analysis test reveal that 93.42 % of the combinational pairs of different sampling and feature selection approaches demonstrated a significant variance in the performance of the distinct combinations of sampling and feature selection techniques. The empirical result indicates the performance of the SFP Model is adversely impacted by class imbalance and irrelevance. The outcome indicates for more than 75% of projects, the performance of trained models improved with an AUC value between a range of 0.805 to 0.99 post-application of sampling and feature selection strategies, in comparison without the use of feature selection and sampling techniques. [ABSTRACT FROM AUTHOR]

Feature selection is very important for pattern recognition to reduce the dimensions of data and to improve the efficiency of learning algorithms. Recent research on new approaches has focused mostly on improving accuracy and reducing computing time. This paper presents a flexible feature-selection method based on an optimized kernel mutual information (OKMI) approach. Mutual information (MI) has been applied successfully in decision trees to rank variables; its aim is to connect class labels with the distribution of experimental data. The use of MI removes irrelevant features and decreases redundant features. However, MI is usually less robust when the data distribution is not centralized. To overcome this problem, we propose to use the OKMI approach, which combines MI and a kernel function. This approach may be used for feature selection with nonlinear models by defining kernels for feature vectors and class-label vectors. By optimizing the objection equations, we develop a new feature-selection algorithm that combines both MI and kernel learning, we discuss the relationship among various kernel-selection methods. Experiments were conducted to compare the new technique applied to various data sets with other methods, and in each case the OKMI approach performs better than the other methods in terms of feature-classification accuracy and computing time. OKMI method solves the problem of computation complexity in the probability of distribution, and avoids this problem by finding the optimal features at very low computational cost. As a result, the OKMI method with the proposed algorithm is effective and robust over a wide range of real applications on expert systems. [ABSTRACT FROM AUTHOR]

In the past, most historical research has been manually carried out by exploring historical facts reading between the lines of documents. Nowadays, historical big data has become electronically available and advances in machine learning techniques allow us to analyze the vast amount of historical data. From a historical perspective, making inferences about political stances of historical figures is important for grasping historical rivalries and power structures of an era. Thus, in this paper, we propose an approach to the systematic inference of power mechanisms based on a human network constructed from historical data. In this network, humans are linked according to the degree of kinship using genealogy records, and identified by political stances on agendas recorded in the annals of a dynasty as a political force. And then, a machine learning algorithm, semi-supervised learning, classifies humans who cannot identify political stances as political forces that reflect the links of the networks. The data consist of the genealogy of the Andong Gwon clan, a record of family relations of 10,243 people from the 10th to 15th century Korea, and the Annals of the Joseon Dynasty, a historical volume that describes historical facts of the Joseon Dynasty for 472 years and is composed of 1894 fascicles and 888 books. From the data, we construct a human network based on a historically meaningful period (1443–1488), and classify people into two political forces using the proposed method. We suggest that this machine learning approach to historical study could be utilized as a potent reference tool devoid of the subjectivism of human experts in the field of history. [ABSTRACT FROM AUTHOR]

Feature selection is an effective pre-processing step to remove possible redundant and irrelevant features for various machine learning paradigms, which can help build a more understandable machine learning based component in hybrid expert systems. Single-label quadratic programming feature selection (QPFS) model is formulated as a QP problem with a unit simplex constraint to both minimize feature-feature redundancy and maximize feature-label relevance simultaneously. Since its redundancy matrix is desired not to be positive semi-definite and then estimated by Nystrom low-rank approximation method, its performance depends greatly on under-sampling rate and random permutation. In this paper, without any approximation, we extend this model to construct a regularized version (rQPFS), resulting in a strictly convex QP problem, to achieve a globally optimal subset of features. To reduce the increment of computational time for entire redundancy matrix, Frank–Wolfe method with a sub-linear convergence rate and its special cases for strictly convex function and unit simplex constraint are applied to solve our rQPFS efficiently. Furthermore, to tackle multi-label FS paradigm, pruned problem transformation trick is used to evaluate feature-label relevance to describe label correlations sufficiently. The detailed experimental study on eight data sets shows that our proposed method performs the best, compared with six state-of-the-art multi-label FS methods, according to six instance-based performance evaluation measures. [ABSTRACT FROM AUTHOR]

In multiword expression (MWE) recognition, there exist many studies where different learning methods are employed to decide whether given word combination is a multiword expression. The recognition methods commonly utilize a number of features that are extracted from a data source, frequently from the given text. Though the recognition methods and the features are well studied, we believe that to achieve the best possible performance with a learning method, different subsets of features should also be considered and the best performing subset must be selected. In this paper, we propose a procedure that covers the performance comparison of well-known feature selection methods to obtain the best feature subset in MWE recognition. The evaluation tests are performed on a Turkish MWE data set and the performance is measured by precision, recall and F1 values. The highest F1 value =0.731 is obtained by C4.5 classifier employing either wrapper or filtering method in feature selection. In the regarding setting(s), it is examined that the performance is increased by 1.11% compared to the setting where all features are employed in classification. Based on the experimental results, it may be stated that feature selection improves the performance of MWE recognition by eliminating the noisy/non-effective features. Moreover, it is obvious that proposed feature selection method contributes to the overall MWE recognition system by reducing the measurement and storage requirements due to the lower number of features in classification, providing a faster and more-cost effective learning model. [ABSTRACT FROM AUTHOR]

In today's era of data-driven digital society, there is a huge demand for optimized solutions that essentially reduce the cost of operation, thereby aiming to increase productivity. Processing a huge amount of data, like the Microarray based gene expression data, using machine learning and data mining algorithms has certain limitations in terms of memory and time requirements. This would be more concerning, when a dataset comes with redundant and non-important information. For example, many report-based medical datasets have several non-informative attributes which mislead the classification algorithms. To this end, researchers have been developing several feature selection algorithms that try to discard the redundant information from the raw datasets before feeding them to machine learning algorithms. Metaheuristic based optimization algorithms provide an excellent option to solve feature selection problems. In this paper, we propose a music-inspired harmony search (HS) algorithm based wrapper feature selection method. At the beginning, we use a chaotic mapping to initialize the population of the HS algorithm in order to better coverage of the search space. Further to complement the inferior exploitation of the HS algorithm, we integrate it with the Late Acceptance Hill Climbing (LAHC) method. Thus the combination of these two algorithms provides a good balance between the exploration and exploitation of the HS algorithm. We evaluate the proposed feature selection method on 15 UCI datasets and the obtained results are found to be better than many state-of-the-art methods both in terms of the classification accuracy and the number of features selected. To evaluate the effectiveness of our algorithm, we utilize a combination of precision, recall, F1 score, fitness value, and execution time as performance indicators. These metrics enable us to obtain a comprehensive assessment of the algorithm's abilities and limitations. We also apply our method on 3 microarray based gene expression datasets used for prediction of cancer to ensure the scalability and robustness as a feature selection method in real-life scenarios. In addition to this, we test our approach using the COVID-19 dataset, and it performs better than several metaheuristic based optimization techniques. • A novel metaheuristic based feature selection method is proposed. • Harmony Search Algorithm is combine with Late Acceptance Hill-Climbing method. • Chaotic map is used to for proper exploration of Harmony Search Algorithm. • Method is applied on many UCI and Microarray based Gene Expression datasets. • Achieved satisfactory results in terms of classification score and feature reduction. [ABSTRACT FROM AUTHOR]

The diagnosis of cardiovascular diseases is quite important in the field of medical community. An important physiological signal of human body is heart sound and it arises due to the blood turbulence and pulsing of cardiac structures. For the early diagnosis of heart diseases, the analysis of heart sounds play an important role as they contain a huge quantity of pathological information associated with heart. To detect heart sounds, Phonocardiogram (PCG) is used as it is a highly useful and non-invasive technique and can be easily analyzed well. In this paper, some efficient models are proposed for the classification of PCG signals. Two important and robust conglomerated models are proposed initially, wherein the first strategy utilizes the concept of semi-supervised Non-negative Matrix Factorization (NMF) along with Brain Storming (BS) optimization algorithm and an advanced version of BS termed as Advanced BS (ABS) is proposed and then it is merged with Genetic Programming (GP) so that new algorithms such as BS-GP and ABS-GP are formed and finally the features selected through it are fed to classification through machine learning. The second strategy utilizes the concept of using three dimensionality reduction techniques along with Fuzzy C-means (FCM) clustering and then an Advanced Sine-Cosine (ASC) optimization algorithm with three different modifications is proposed for the purpose of feature selection and finally it is classified. Deep learning techniques were also employed in the study such as the usage of an Attention based Bidirectional Long Short-Term Memory (A-BLSTM), Ordinal Variational Autoencoder (O-VAE), Conditional Variational Autoencoders (CVAE), Hyperspherical CVAE (H-CVAE) and the Restricted Boltzmann Machine based Deep Belief Network (RBM-DBN) for the classification of PCG signals. The experiment is conducted on a publicly available dataset and results show that a high classification accuracy of 95.39% is obtained for the semi-supervised NMF concept with ABS-GP technique and Support Vector Machine (SVM) classifier. [ABSTRACT FROM AUTHOR]

Several feature selection methods have been developed to extract the optimal features from a dataset in medical datasets classification. Creating an efficient technique has become a challenge because of the high dimensions, noise, and redundant information. In this paper, we propose a hybrid filter-wrapper approach for feature selection. An ensemble of filter methods, ReliefF and Fuzzy Entropy (RFE) is developed, and the union of top-n features from each method are considered. The Equilibrium Optimizer (EO) technique is combined with Opposition Based Learning (OBL), Cauchy Mutation operator and a novel search strategy to enhance its capabilities. The OBL strategy improves the diversity of the population in the search space. The Cauchy Mutation operator enhances its ability to evade the local optima during the search, and the novel search strategy improves the exploration capability of the algorithm. This enhanced form of EO is integrated with eight time-varying S and V-shaped transfer functions to convert the solutions into binary form, Binary Enhanced Equilibrium Optimizer (BEE). The features from the ensemble are given as input to the Binary Enhanced Equilibrium Optimizer to extract the essential features. Fuzzy KNN based on Bonferroni mean is used as the learning algorithm. Twenty-two benchmark datasets and four microarray datasets are used to test the algorithm's efficiency. This method is also applied to a COVID-19 case study. The results demonstrate the superiority of the proposed approach, RFE-BEE, among other methods in terms of fitness values, accuracy, precision, sensitivity, and F-measure, among several other state-of-the-art algorithms. RFE-BEE can be applied to various biomedical, computer vision and engineering applications such as electromyography pattern recognition, COVID-19 diagnosis, face recognition and fault diagnosis. [ABSTRACT FROM AUTHOR]

With the increasing use of sensors in almost every device and application worldwide, the volume of data increases exponentially. Instance selection is an important data preprocessing step that can be applied to many machine learning models. It is even crucial for executing machine learning tasks in constrained environments as demanded by many Internet of Things (IoT) enabled applications today such as the Human Activity Recognition (HAR) domain of applications. In this paper, we have proposed a hybrid selection and training pipeline methodology that combines the nearest neighbor concept with evolutionary computing to address the instance selection problem of smartphone sensing-based HAR. A clustering algorithm has been proposed first followed by a Genetic Algorithm based instance selection approach. It is so far the first of its kind in the aforementioned domain of HAR based on smartphone sensing data. The proposed instance selection has been tested on two of the popular benchmark HAR datasets — UCI-HAR and WISDM. The experimented outputs report that the proposed method has effectively reduced the dataset size by around 40% for the benchmark datasets while retaining the recognition accuracy above 94%. This is a clear depiction of the removal of the outliers from the instance set. The proposed approach has also been compared with other state-of-the-art under-sampling approaches and the results show that our approach has performed better. • Tuple selection pipeline for Human Activity Recognition in constrained environments. • Combination of unsupervised approach with Genetic Algorithm for Instance selection. • 40% reduction of instances while retaining accuracy above 94%. • Proposed pipeline performs better than state-of-the art undersampling approaches. • Hardness property of instances is found to decrease. [ABSTRACT FROM AUTHOR]

• Machine learning classifiers can assess the credibility of financial stock tweets. • Classifiers trained with novel financial features result in improved performance. • Random Forest classifier outperforms other classifiers but depend on more features. Social media plays an important role in facilitating conversations and news dissemination. Specifically, Twitter has recently seen use by investors to facilitate discussions surrounding stock exchange-listed companies. Investors depend on timely, credible information being made available in order to make well-informed investment decisions, with credibility being defined as the believability of information. Much work has been done on assessing credibility on Twitter in domains such as politics and natural disaster events, but the work on assessing the credibility of financial statements is scant within the literature. Investments made on apocryphal information could hamper efforts of social media's aim of providing a transparent arena for sharing news and encouraging discussion of stock market events. This paper presents a novel methodology to assess the credibility of financial stock market tweets, which is evaluated by conducting an experiment using tweets pertaining to companies listed on the London Stock Exchange. Three sets of traditional machine learning classifiers (using three different feature sets) are trained using an annotated dataset. We highlight the importance of considering features specific to the domain in which credibility needs to be assessed for – in the case of this paper, financial features. In total, after discarding non-informative features, 34 general features are combined with over 15 novel financial features for training classifiers. Results show that classifiers trained on both general and financial features can yield improved performance than classifiers trained on general features alone, with Random Forest being the top performer, although the Random Forest model requires more features (37) than that of other classifiers (such as K-Nearest Neighbours − 9) to achieve such performance. [ABSTRACT FROM AUTHOR]

In Data Mining, during the preprocessing step, there is a considerable diversity of candidate algorithms to select important features, according to some criteria. This broad availability of algorithms that perform the Feature Selection task gives rise to the difficulty of choosing, a priori , between the algorithms at hand, the most promising one for a particular problem. In this paper, we present the proposal and evaluation of a new architecture for the recommendation of Feature Selection algorithms based on the use of Metalearning. Our framework is very flexible since the user can adapt it to its proper needs. This flexibility is one of the main advantages of our proposal over other approaches in the literature, which involve steps that cannot be adapted to the user’s local requirements. Furthermore, it combines several concepts of intelligent systems, including Machine Learning and Data Mining, with topics derived from expert systems, as user and data-driven knowledge, with meta-knowledge. This set of solutions coupled with leading-edge technologies allows our architecture to be integrated into any information system, which impact on the automation of services and in reducing human effort during the process. Regarding the Metalearning process, our framework considers several types of properties inherent to the data sets, as well as, Feature Selection algorithms based on many information, distance, dependence and consistency measures. The quality of the methods for Feature Selection was estimated according to a multicriteria performance measure, which guided the ranking process of these algorithms for the construction of data metabases. Proposed by the authors of this work, this multicriteria performance measure combines any three measurements on a single one, creating an interesting and powerful tool to evaluate not only FS algorithms but also to assess any context where it is necessary a combination to maximize a measure or minimize it. The recommendation models, represented by decision trees and induced from the training metabases, allowed us to see in what circumstances a Feature Selection algorithm outperforms the other and what aspects of the data present greater influence in determining the performance of these algorithms. Nevertheless, if the user wishes, any other learning algorithm may be used to induce the recommendation model. This versatility is another strong point of this proposal. Results show that with the characterization of data, through statistical, information and complexity measures, it is possible to reach an accuracy higher than 90%. Besides yielding recommendation models that are interpretable and robust to overfitting, the developed architecture is less computationally expensive than approaches recently proposed in the literature. [ABSTRACT FROM AUTHOR]

Classification tasks are of great importance in machine learning. However, class imbalance is a universal problem that needs to be solved in classification and can greatly affect the performance of machine learning classifiers. Developing from the basic belief rule base (BRB) system, the hierarchical belief rule-based system can integrate expert knowledge and has the potential to alleviate the negative effect of class imbalance. To utilize BRB to solve the imbalanced multi-classification task and avoid the combinational explosion problem, a novel hierarchical BRB structure based on the extreme gradient boosting (XGBoost) feature selection method, abbreviated as HFS-BRB is proposed in this paper in order to deal with any number of classes. In the hierarchical BRB structure, there is one main-BRB in the first level and several sub-BRBs in the second level. The XGBoost technique is used for feature selection in the modelling process of each abovementioned BRB model. The output of the main BRB represents the approximated classification between confusable classes. Then, these samples were transmitted to a certain sub-BRB for binary classification to make a precise prediction. Thus, a multi-classification problem can be transformed into several binary classification problems. The class imbalance is alleviated. To test the effectiveness of the proposed method, seven classical benchmark problems for imbalanced classification and a real asteroid orbit classification were performed. [ABSTRACT FROM AUTHOR]

• A multi-objective online multi-label feature selection method is proposed. • A novel metric based on mutual information was proposed to evaluate features. • A novel algorithm was designed to update a fixed-sized feature pool. • Features are added to the pool or removed from the pool based on their importance. • Our method assigns a high rank to this highly relevant and non-redundant feature. Multi-label classification methods aim at assigning more than one label to each instance. In many real-world classification problems such as image multi-label classification tasks such as cancer detection, and text classification, we faced with thousands of thousand features. The performance of machine learning methods will be reduced while faced with high dimensional problems. To tackle this issue, feature selection methods are introduced to choose a small set of prominent features which best describe the data. Traditional multi-label feature selection methods are required to access to whole feature space, while in online platforms such as Facebook and Twitter, we faced with streams of data added by the users of these platforms over the time. Traditional multi-label feature selection methods are failed while applied on data streams. To solve this issue, online methods are introduced to deal with data streams. Existing streaming multi-label feature selection methods consider the task as a single optimization process while there are several contradictory objectives that need to be optimize simultaneously. To solve this issue, this paper uses a multi-objective search strategy to choose streaming features by using the mutual information and Pareto optimal set theories. There are several objectives such as minimizing the redundancy of features, and maximizing the relevancy of features to a set of labels that are need to be optimized during the feature selection process. Here, we used the Pareto set theory to identify a set of no-dominant solutions which best describe the problem. The proposed method has compared with a set of state-of-the-art online feature selection methods and the obtained results demonstrate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

The curse of dimensionality is a common problem in classification tasks. However, feature selection is an exciting approach to deal with this type of problem by searching for a suboptimal feature set, either by eliminating irrelevant attributes, those with redundant information, or even both. Furthermore, although the use of machine learning techniques for the classification of drum cymbals is found in the literature, little attention has been paid to approaches that focus on classifying these instruments according to their bronze alloys. This paper aims to explore and evaluate the temporal information retrieved from audios, using TSFEL (Time Series Feature Extraction Library) as a tool to extract 18 temporal attributes, three feature selection approaches to assess these features, and logistic regression as a classifier. To this end, 276 audios referring to four drum cymbals of three different bronze alloys were captured in the studio through a sound acquisition procedure that took into account environment and microphone variations. Hence, one expects to find an optimal subset of features that contains enough information from the audios to achieve the best classification performance in the proposed problem. The experimental results show that a feature selection approach sequentially based on L 1 Regularization and Correlation Analysis was able to find a subset consisting of 5 attributes that achieved an average accuracy of 97.08%. • The computational model can identify the cymbals' bronze alloys through their sound. • A standardized impact procedure is developed for the audio signals acquisition. • Feature selection could reduce the dimensionality up to 70%, improving accuracy. • Logistic Regression with feature selection achieved high material classification rate. • High accuracy is attained even considering different sound capture perspectives. [ABSTRACT FROM AUTHOR]

• Introduces a hybrid forecasting framework to identify enterprise financing risk. • Develops a feature selection module to identify the optimal input variables. • Proposes an improved ELM prediction model for enterprise financing risk. • Demonstrates the framework's reliability in forecasting enterprise financing risk. A scientific framework that can effectively forecast enterprise financing risks can both promote enterprise management and reduce the cost of risk for financial institutions. This study constructs a novel hybrid forecasting framework for enterprise financing risk incorporating modules for data preprocessing, feature selection, forecasting, and evaluation. Specifically, the data preprocessing module mainly realizes the prescreen financing risk indicators and solves the forecasting challenge created by imbalanced data; The feature selection module based on binary grey wolf optimization is designed to intelligently identify optimal financing risk indicators; The forecasting module based on the improved extreme learning machine model established in this paper achieves higher forecasting accuracy; and the evaluation module provides reasonable and scientific evaluations of the proposed hybrid forecasting framework by using the data from small and medium-sized enterprises (SMEs) in China and all listed enterprises with Shanghai and Shenzhen A-shares. Using the SMEs dataset as an example, the Type-2 error value of the developed hybrid forecasting framework is 0.1765, which is 70.24% lower than the average result of the other models; the G-mean value of the framework is 0.8566, which is 40.56% higher than the average result of the other models. Based on the results, the proposed hybrid forecasting framework outperforms other comparative models and is a reliable tool for forecasting enterprise financing risk. [ABSTRACT FROM AUTHOR]

The goal of few-shot learning is to use limited labeled samples to complete independent classification tasks. The feature extractor of few-shot learning needs to have a stronger feature expression ability to generalize in unseen novel classes. To further enhance the expressive ability, in this paper, we propose an inherited feature extraction method, named Base and Meta Feature Extraction (BMFE). Base feature represents the task-irrelevant classification information of each sample. Meta feature obtained by the proposed Triplet Meta-train Mechanism (TMM) inherits the classification information and also contains the task-related meta information of each sample. We concatenate both the base and meta features to complementarily express the rich information of each sample. Besides, instead of relying on limited support samples to obtain the prototype, we propose a novel unsupervised prototype correction module, named Prototype Self-updating (PSU). All unlabeled query samples in a few-shot test task participate in the iterative updating of each prototype in the task without training. Extensive experiments prove that our overall method can obtain richer features by BMFE and more accurate prototypes by PSU. Our overall method outperforms state-of-the-art methods on miniImageNet and tiredImageNet datasets, and especially under the 1-shot case we obtains 78.45% and 81.21% classification accuracy respectively. • BMFE can obtain the complementary features representation of FSL. • TMM mechanism can better perform meta-training of BMFE. • PSU can correct prototypes without training process. • The proposed CFPSU gets outstanding results on two standard datasets. [ABSTRACT FROM AUTHOR]

• Using an optimal cost-sensitive credit scoring. • Using a hybrid performance metric to calculate the probability of correct scoring. • Using an optimal feature selection technique to nine different scenarios. • A policy advice to banking activists to resort to a combination of models. This paper aims to provide an optimal credit rating by using a new hybrid performance metric, including several performance metrics, referred to as the probability of credit scoring correctness. There have been several credit-scoring models proposed in the banking system. These models, either single classifiers or hybrid and ensemble models determine the class of customers (good or bad accounts). The banks need the probability of default to calculate the expected loss and the unexpected loss and then determine whether their capital is adequate. Generally speaking, the proposed model can utilize n classifiers and m performance metrics, the larger n , and m , the more diverse and more reliable the model is as an estimate of customer classes. We evaluated the proposed model using five common classifiers, namely k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), AdaBoost, and probabilistic neural network (PNN), and eight performance metrics. To extract the feature, an optimization approach using genetic algorithms and nine different scenarios were used. We have then calculated the optimal parameters of each algorithm simultaneously by defining the cost-sensitive objective function based on the probability of correctly classifying it into two classes 0 (good) and 1 (bad). We evaluated the performance of the proposed hybrid model using the Iranian dataset and the UCI German and Australian datasets. The results indicate the proposed method provides very good scoring performance. Using this approach, the bank can filter its bad customers based on the tolerance it considers for the probability value, so it can decide whether it should provide loans to that customer. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the leading killers of women around the world. Finding compounds with good bioactivity, metabolic dynamics and safety, including Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET for short), is a long and challenging task in breast cancer therapy. In the paper, molecular descriptor data of compounds was analyzed by the ensemble learning algorithm, and important features were selected for the development and validation of ADMET classification models. The overall process includes data cleaning, data splitting to training and testing sets, feature selection and classification model evaluation. A Two-Level Stacking Algorithm (TLSA) based on ensemble learning is proposed for ADMET classification. Various performance measures like classification accuracy, precision, recall, confusion matrix, F1-score, Receiver Operating Characteristic (ROC) curves and the Area Under the ROC Curves (AUC) are reported to show the superiority of the proposed method as compared to different classifiers. The experimental results show that the second level algorithm for TLSA utilizes Logistic Regression is better than other classifiers for the properties of Absorption, Distribution and Excretion, with accuracy of 94.6037%, 94.9410% and 88.1956% respectively. For the properties Metabolism and Toxicity, the second level algorithm utilizes Support Vector Machine to achieve the best classification performance, with accuracy of 88.8702% and 96.7960% respectively. The results show that the proposed approach works well with the classification of compound properties and can be a good alternative for the well-known machine learning program. • A novel TLSA model for ADMET classification of compounds is proposed. • The proposed method improves the accuracy of classification results. • Applying a wide range of evaluation metrics to ensure the model's reliability. • Most important molecular descriptors of compounds were selected using RF. [ABSTRACT FROM AUTHOR]

Disease diagnosis and prediction methods in biotechnology and medicine have significantly advanced over time. Consequently, analyzing raw gene expression is crucial for identifying diseases such as cancer. Interestingly, microarrays are a tool that records gene expression from deoxyribonucleic acid (DNA) or ribonucleic acid. This technique exhibits intriguing characteristics, such as generating high-dimensional data with a small sample size. However, in the case of such dataset, the classification model is prone to overfitting. This limitation can be overcome by reducing the dimensions of the microarray datasets to a reasonable number. Machine learning (ML)-based data reduction has recently achieved considerable attention in genomic research. Therefore, this review examines recent studies that present state-of-the-art data reduction and classification algorithms for microarray gene expression data to diagnose tumors and analyzes their performance. To the best of our knowledge, this is the first review that provides a comprehensive view of data preprocessing, dimensionality reduction, including feature (i.e., gene) selection, feature extraction, and their hybrid, and ML algorithms. The paper is structured as follows. First, this review summarizes several data preprocessing methods applied to gene expression datasets. Then, a detailed review of various ML-based feature selection algorithms, including filter, wrapper, embedded, ensemble, and hybrid algorithms, is discussed. These algorithms are examined under three main classes—supervised, unsupervised, and semisupervised ML. Next, the feature extraction and hybrid of feature extraction and selection algorithms are thoroughly reviewed. Furthermore, a detailed review of broadly applied ML algorithms to simplify tumor and nontumor classification using microarray datasets is presented. Finally, the challenges and open questions related to gene expression datasets for accurate cancer classification and detection are highlighted. [Display omitted] • A comprehensive review on microarray gene expression reduction is proposed. • A novel taxonomy of up-to-date gene reduction algorithms is presented. • The strength and limitations of each type of gene reduction algorithms are provided. • Provide an outline of the accurately used type of machine learning algorithms. • Analyze the performance of the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Paroxysmal atrial fibrillation (PAF) is a cardiac arrhythmia which is often challenging to diagnose because patients may be asymptomatic, and episodes are usually intermittent. In this paper we describe a method for identifying patients with a history of PAF using electrocardiogram (ECG) recordings during sinus rhythm. We analyzed, on a beat-to-beat basis, the P-waves in the sinus rhythm ECGs of 69 patients with a history of PAF and 59 healthy individuals. From each subject's P-waves, we calculated key electrocardiographic metrics, including some which are proposed here for the first time. Using means testing and feature selection methods, we discerned the features which were most useful for classification, and trained a Random Forest which identified PAF patients. Our approach achieved a 93.45% accuracy, sensitivity of 95.21%, and specificity of 91.40% using P-wave integral and novel amplitude and slope-based features which ranked highest in importance compared to other metrics from the literature. In particular, descriptive statistics of P-wave amplitudes, slopes, and integrals, were effective for identifying subjects with PAF history vs. healthy individuals. Our method has a high sensitivity and discrimination ability, with an AUC=0.9669 which is superior to others', and can thus be potentially valuable for the early identification of patients who are prone to episodes of PAF, even as part of the standard cardiological checkup that most adults undergo periodically. • Early identification of PAF during sinus rhythm with high accuracy. • P-wave signal metrics on a beat-to-beat basis are proposed. • P-wave amplitude, integral and slope features were ranked as the most important. • A RF classifier distinguishes PAF from healthy subjects with a state-of-the-art AUC. [ABSTRACT FROM AUTHOR]

Customer support constitutes the face of a brand and plays a critical role in shaping customers retention and loyalty. To facilitate customer contacts and to reduce the corresponding operating costs, organizations resort to self-service interaction channels. However, dealing with self-service customer support platforms has been identified to be one of the most frustrating aspects of a poor customer contact experience. This paper builds on real data from contact centers of a major U.S. insurance company, covering about 10 million calls, to study transfer rate at the interactive voice-response platform. We develop an empirical model to analyze features that exert significant impact on the likelihood that a call arrived to the platform is eventually transferred to a live agent. We find evidence that caller types, specific caller intents, trying distinct channels, and payment attempts can help predicting the transfer outcome. Our study confirms the importance of a caller's location attribute and evinces that the size and composition of the set of effective features and their directions vary substantially across different states. Our results provide managers with several actionable insights to enhance the customer contact experience. For example, our feature selection analysis enables designing personalized interactive voice-response systems based on customer-contact features. Identifying those calls expected to be transferred at any time can guide recruiting and scheduling a workforce with appropriate skill sets. In addition, the transfer rate is a key input variable for various decision making problems arising in the management of call center operations. • Analyzing feature selection on individual-level call transfer rate using big data. • Developing a machine learning model to predict a customer's call transfer outcome. • Formulating actionable insights to introduce personalized customer support systems. [ABSTRACT FROM AUTHOR]

• The current study extracts higher-order features from fire images. • A new set of data is correctly labelled for classifying fire and non-fire. • Information-theoretic feature selection is adopted to minimize computational cost. • The SVM performs classification with an RBF kernel. • The model draws an overall accuracy of 96,21%, and a specificity of 97,99%. • The model draws an f-measure and g-mean values of 96,13% and 96,19% respectively. In recent years, wildfires and forest fires have ravaged millions of hectares of the forest all over the world. Recent technological breakthroughs have increased interest in computer vision-based fire classification that classifies fire and non-fire pixels from image or video datasets. Fire pixels from an image or video can be classified using either a traditional machine learning approach or a deep learning approach. Presently, the deep learning approach is the mainstream in forest fire detection studies. Although deep learning algorithms can handle vast amounts of data, they ignore the variation in complexity among training samples and as a result, their training model performance is limited. Furthermore, deep learning approaches with little data and features perform poorly in real-world challenging fire scenarios. As a result, the current study adopts a machine learning technique to extract higher-order features from the processed images from the publicly available datasets: Corsican dataset and FLAME, and a private dataset: Firefront_Gestosa, for classifying fire and non-fire pixels. It should be emphasized that in machine learning applications, handling multidimensional data to train a model is challenging. Feature selection is used to overcome this problem by removing redundant or irrelevant data that has an impact on the model's performance. In this paper, information-theoretic feature selection approaches are used to choose the most important features for classification while minimizing the computational cost. The traditional machine classifier, Support Vector Machine (SVM) is adopted in the present work, that works on the discriminative features input selected from the feature selection technique. The SVM performs the classification of fire and non-fire pixels with a Radial Basis Function (RBF) kernel, and the model's performance is measured using assessment measures such as overall accuracy, sensitivity, specificity, precision, recall, F-measure, and G-mean. The model draws an overall accuracy of 96,21%, a sensitivity of 94,42%, a specificity of 97,99%, a precision of 97,91%, a recall of 94,42%, an f-measure and g-mean values of 96,13% and 96,19% respectively. [ABSTRACT FROM AUTHOR]

• We present a novel Alzheimer's disease prediction model based on deep neural network. • Integrating heterogeneous omics data contributed to improving the performance. • Deep learning with biological feature selection outperformed traditional approaches. The molecular mechanism of Alzheimer's disease (AD) has not been clearly revealed and there is no clinically reliable genetic risk factor. Therefore, diagnosis of AD has been mostly performed by analyzing brain images such as magnetic resonance imaging and neuropsychological tests. Identifying the molecular-level mechanism of AD has been lacking data owing to the difficulty of sampling in the posterior brains of normal and AD patients; however, recent studies have produced and analyzed large-scale omics data for brain areas such as prefrontal cortex. Therefore, it is necessary to develop AD diagnosis or prediction methods based on these data. This paper proposed a deep learning-based model that can predict AD using large-scale gene expression and DNA methylation data. The most challenging problem in constructing a model to diagnose AD based on the multi-omics dataset is how to integrate different omics data and how to deal with high-dimensional and low-sample-size data. To solve this problem, we proposed a novel but simple approach to reduce the number of features based on a differentially expressed gene and a differentially methylated position in the multi-omics dataset. Moreover, we developed a deep neural network-based prediction model that improves performance compared to that of conventional machine learning algorithms. The feature selection method and the prediction model presented in this paper outperformed conventional machine learning algorithms, which utilize typical dimension reduction methods. In addition, we demonstrated that integrating gene expression and DNA methylation data could improve the prediction accuracy. https://github.com/ChihyunPark/DNN_for_ADprediction. [ABSTRACT FROM AUTHOR]

• An investigation into an automatic computational framework for lateral flow assays. • Critical examination of the data structure, features and algorithms. • Pseudo control colours were proposed as a new feature within the optimal feature set. • Proposed scheme offers high accuracy and fulfils ASSURED criteria. This paper aims to deliberately examine the scope of an intelligent colourimetric test that fulfils ASSURED criteria (Affordable, Sensitive, Specific, User-friendly, Rapid and robust, Equipment-free, and Deliverable) and demonstrate the claim as well. This paper presents an investigation into an intelligent image-based system to perform automatic paper-based colourimetric tests in real-time to provide a proof-of-concept for a dry-chemical based or microfluidic, stable and semi-quantitative assay using a larger dataset with diverse conditions. The universal pH indicator papers were utilised as a case study. Unlike the works done in the literature, this work performs multiclass colourimetric tests using histogram-based image processing and machine learning algorithm without any user intervention. The proposed image processing framework is based on colour channel separation, global thresholding, morphological operation and object detection. We have also deployed aserver-based convolutional neural network framework for image classification using inductive transfer learning on a mobile platform. The results obtained by both traditional machine learning and pre-trained model-based deep learning were critically analysed with the set evaluation criteria (ASSURED criteria). The features were optimised using univariate analysis and exploratory data analysis to improve the performance. The image processing algorithm showed >98% accuracy while the classification accuracy by Least Squares Support Vector Machine (LS-SVM) was 100%. On the other hand, the deep learning technique provided >86% accuracy, which could be further improved with a large amount of data. The k-fold cross-validated LS-SVM based final system, examined on different datasets, confirmed the robustness and reliability of the presented approach, which was further validated using statistical analysis. The understaffed and resource-limited healthcare system can benefit from such an easy-to-use technology to support remote aid workers, assist in elderly care and promote personalised healthcare by eliminating the subjectivity of interpretation. [ABSTRACT FROM AUTHOR]