Search

Computer-aided diagnosis (CAD) techniques, exemplified by chest x-ray (CXR)-based methods, offer a cost-effective alternative for early-stage COVID-19 diagnosis compared to expensive options such as polymerase chain reaction (PCR) and computed tomography (CT) scan. Despite efforts to diagnose COVID-19 with CXR-based methods, their performance could be improved by considering the spatial relationships between regions of interest (ROIs) in CXR images. This oversight hinders the ability to accurately identify areas of the human lung most vulnerable to COVID-19. This model implements a two-way classification system to differentiate between lung X-ray impressions, accurately determining whether they are affected or normal. The effectiveness of this system is assessed using metrics such as accuracy, recall, precision, and F1-score. We employed over 2409 samples of X-ray images in the COVID-19 diagnosis process. The results obtained from the VGG16 model showcase outstanding performance, with a recognition rate of 99.58% for X-ray images and 94.29% for CT-scan pictures within the given sample size and two-class categorization. This model surpasses all existing approaches documented in the literature. Medical professionals and healthcare workers can effectively utilize this proposed system, leveraging X-rays and CT scans of human lungs to identify COVID-19 cases accurately. [ABSTRACT FROM AUTHOR]

For high-dimensional classification, interpolation of training data manifests as the data piling phenomenon, in which linear projections of data vectors from each class collapse to a single value. Recent research has revealed an additional phenomenon known as the 'second data piling' for independent test data in binary classification, providing a theoretical understanding of asymptotically perfect classification. This paper extends these findings to multi-category classification and provides a comprehensive characterization of the double data piling phenomenon. We define the maximal data piling subspace, which maximizes the sum of pairwise distances between piles of training data in multi-category classification. Furthermore, we show that a second data piling subspace that induces data piling for independent data exists and can be consistently estimated by projecting the negatively-ridged discriminant subspace onto an estimated 'signal' subspace. By leveraging this second data piling phenomenon, we propose a bias-correction strategy for class assignments, which asymptotically achieves perfect classification. The present research sheds light on benign overfitting and enhances the understanding of perfect multi-category classification of high-dimensional discrimination with a help of high-dimensional asymptotics. [ABSTRACT FROM AUTHOR]

This paper describes a method for generating realistic synthetic images using a generative deep learning model. When generating training examples, the features of climate, relief, architecture, and landscape of a particular area are considered. The generative model is represented by a variational autoencoder consisting of an encoder, a latent space, and a decoder. To train the autoencoder, a new dataset CSCDroneCL_v1 was created, consisting of manually labeled panoramic images and training examples selected from the open VisDrone2022 dataset. Based on the SOTA-ConvNet template, a discriminative neural network model was designed and trained to classify anomalies obtained from multitemporal panoramas. Architectural compatibility between generative and discriminative models is ensured by using a single design pattern. The unified architecture of neural network models made it possible to apply the transfer learning method. The use of pretrained parameters in the variational autoencoder model makes it possible to compensate for the small size of the CSCDroneCL_v1 dataset. The generated synthetic training examples of panoramas were added to the VisDrone2022 dataset. Using the new dataset, a discriminative model was trained, resulting in an increase in anomaly classification accuracy by 21.2% for the selected class. [ABSTRACT FROM AUTHOR]

Here, we propose a method for the semantic segmentation of 3D point clouds based on functional data analysis. For each point of a training set, a number of handcrafted features representing the local geometry around it are calculated at different scales, that is, varying the spatial extension of the local analysis. Calculating the scales at small intervals allows each feature to be accurately approximated using a smooth function and, for the problem of semantic segmentation, to be tackled using functional data analysis. We also present a step-wise method to select the optimal features to include in the model based on the calculation of the distance correlation between each feature and the response variable. The algorithm showed promising results when applied to simulated data. When applied to the semantic segmentation of a point cloud of a forested plot, the results proved better than when using a standard multiscale semantic segmentation method. The comparison with two popular deep learning models showed that our proposal requires smaller training samples sizes and that it can compete with these methods in terms of prediction. [ABSTRACT FROM AUTHOR]

In today's rapidly evolving digital landscape, the surge in smartphone usage is paralleled by an increasing wave of cyberthreats, highlighting the limitations of existing signature-based malware detection methods. To address this problem, our research introduces a Time-Aware Machine Learning (TAML) framework specifically designed for Android malware detection. Our framework extracts the best time-correlated features and then it builds time-aware and time-agnostic machine learning (ML) models. The ML models are trained on the KronoDroid dataset, which contains more than 41,000 benign Android apps and more than 36,000 malicious apps developed between 2008 to 2020. Our experimental evaluation revealed that the Last Modification Date 'LastModDate' feature is a critical variable for time-aware classification. Moreover, our empirical analysis reveals that real-device detection outperforms emulator-based detection. Impressively, the time-correlated features boosts the detection performance and achieving an outstanding 99.98% F1 score in a time-agnostic setting. In addition, on each year, our time-aware experiments outperformed the traditional ML detection models. Our time-aware classifier achieved a 91% F1 score on average and a maximum F1 score of 99% of yearly chunk experiments over 12 years. These experimental results affirm the effectiveness of our proposed method in detecting Android malware. [ABSTRACT FROM AUTHOR]

Forest cover type prediction is used for the forest management organizations. It also get the insight on area of the forest cover up to date and development lack in present time. Classification of the forest area and type of trees could eventually help in maintaining the eco system and to get inference on deforestation. In present scenario this problem gains more attention hence to retain the climate change impact forest cover type and area prediction would help a lot. This paper proposes a novel ensemble machine learning based random de-correlated extra decision tree model for the forest cover type prediction. The tree based classifiers perform well in prediction of the forest cover data. Many researchers use tree based classifiers for the problem. Even though the enhancement of the accuracy seems to be lower in the multi-class classification problem. So, this research proposes the Extra random de-correlated decision tree method for the prediction of the forest cover. The results the multiple de-correlated decision trees are aggregated for the final classification. This proposed method is the ensemble based method. In ensemble machine learning method combines several base optimal results in order to produce one final optimal result. A decision tree follows a simple predictive outcomes based on the series of the cause and effect values. While adopting the decision tree models the user has to follow the factors including the variable on which the decision to be taken and threshold for deciding the class. Instead of depending on one tree for decision making, multiple tree split criteria can be considered. Also these ensemble based machine learning allow to fine tune the predictor variable based on the feature to use and split criteria. The random forest based methods follows the bagging strategy. It has a major role in the split aspect and decision-making aspect in significant manner. This machine learning model decides where to split based on random selection of features. Random forest tree methods have a uniqueness where each split can be done through scrutiny of different features. This paper proposes the ensemble machine learning based random de-correlated extra decision tree model for the forest cover type prediction. This algorithm especially suits the problem for the multiclass classification nature. Forest cover type prediction helps in identifying the wilderness type and total area of the forest predicted and available. The dataset considered for the paper is from the UCI Machine Learning repository. It contains various features including elevation, slop, aspect, vertical and horizontal distance to hydrology, fire points and roadways, hill shade, wilderness area, soil type and cover type. Initially the preprocessing is done in the data set by identifying the missing values, outlier detection and formatting data. Later the exploratory analysis is carried out using the Pearson correlation coefficients aspect. Then three machine learning techniques: Multiclass SVM, Boosting and proposed EMLARDE were deployed. The accuracy of the proposed EMLARDE method outperforms the other two algorithms. The proposed algorithm performs well for this multiclass classification. [ABSTRACT FROM AUTHOR]

Food demand is exponentially increasing due to the increase in population in every country; hence, increasing the yield is one of the focus areas for sustainable agricultural development. Predicting plant disease is one of the measures to increase crop yield and quality, thereby increasing the economy. The present work aims to develop a web-based application built with a deep learning model to detect plant leaf disease using a leaf image and alert farmers with messages. A comparative study was conducted on the data of the PlantVillage dataset for binary and multiclass classifications. Various deep convolutional neural network (CNN) models, such as MobileNet, DenseNet201, ResNet50, Inception V3, and visual geometry group (VGG) 16 and 19, have been compared with a proposed model. Various metrics include precision, recall, classification report, Confusion Matrix, and accuracy. MobileNet is influential among the selected models, with an accuracy of 97.35% and precision and recall of 0.973 each for multiclass classification. The proposed model achieved an accuracy of 99.39% with a loss of 0.0361, precision of 0.989, and a recall of 0.984 for binary classification compared with deep CNN models. A web-based application was created using the MobileNet model for the convenience of sending an email alert to the user regarding plant disease. The research results help improve a country's crop productivity and the overall economy through prompt and precise decision-making on crop diseases. [ABSTRACT FROM AUTHOR]

Social media platforms have gained immense popularity in recent years and are used for various activities such as marketing, news-sharing, and celebrating achievements. However, they are also notorious for spreading hateful and discriminatory content, which can cause harm to individuals and communities. Therefore, it is crucial to detect and remove such content from social media platforms as soon as possible. Although research related to the detection of hate speech and inflammatory content is increasing, studies focused on code-mixed Indian languages are limited. Hence, in this work, we have conducted a comprehensive study, where we have compared the effectiveness of various neural networks, and transformer-based techniques for the detection of hate and objectionable language in social media tweets in Hinglish, Tamil written in English, and Malayalam written in English, to propose the best-performing ensemble model, named as DConvBLSTM-MuRIL. To carry out our experiments, we have created our datasets for the three languages under study and compared the results with already existing datasets. Our proposed weighted ensemble framework outperformed the existing models, achieving better-weighted F1-scores and better accuracy for all the three languages under consideration. [ABSTRACT FROM AUTHOR]

Drones are widespread and actively employed in a variety of applications due to their low cost and quick mobility and enabling new forms of action surveillance. However, owing to various challenges- limited no. of aerial view samples, aerial footage suffers with camera motion, illumination changes, small actor size, occlusion, complex backgrounds, and varying view angles, human action recognition in aerial videos even more challenging. Maneuvering the same, we propose Aerial Polarized-Transformer Network (AP-TransNet) to recognize human actions in aerial view using both spatial and temporal details of the video feed. In this paper, we present the Polarized Encoding Block that performs ( i) Selection with Rejection to select the significant features and reject least informative features similar to Light photometry phenomena and ( ii) boosting operation increases the dynamic range of encodings using non-linear softmax normalization at the bottleneck tensors in both channel and spatial sequential branches. The performance of the proposed AP-TransNet is evaluated by conducting extensive experiments on three publicly available benchmark datasets: drone action dataset, UCF-ARG Dataset and Multi-View Outdoor Dataset (MOD20) supporting with ablation study. The proposed work outperformed the state-of-the-arts. [ABSTRACT FROM AUTHOR]

Cyber security has an increasing importance since the day when information technologies are an invariable part of modern human life. One of the fundamental areas of cyber security is the concept of software security. Security vulnerabilities in software are one of the main reasons for the exploitation of information systems. For this reason, it has been systematically reported, analyzed and classified for a long time, with a protocol established between the states and the stakeholders of the issue at the level. All these processes are carried out manually by humans today. This situation causes errors and delays caused by human nature. Therefore, the current study aims to help the experts and increase the accuracy of the analysis results by speeding up the processes. To achieve this goal, a model is proposed that uses technical explanations of security reports written in natural language. Our model basically proposes a method that uses word embedding approaches and multi-class classification algorithms from natural language processing techniques. In order to compare the proposed model more accurately, the NVD database, which is open to everyone and accepted as a reference, was chosen. In addition, previous studies in the literature and the model we propose were compared. In order for the results of the compared models to be analyzed more accurately, our model was trained with the data sets of the studies it was compared and the results were presented clearly. The proposed method showed estimation success in the range of 87.34–96.25% for CVSS 2.0 metrics, and in the range of 84–90% for CVSS 3.1. This study, in which different word embedding and classification algorithms are used together, is one of the limited studies on the latest version of the official scoring system used for classification of software security vulnerabilities. Moreover, it is the most comprehensive and original study in its field due to the size of the dataset it uses and the number of databases evaluated. [ABSTRACT FROM AUTHOR]

Least squares regression (LSR) has been widely used in the field of pattern recognition. However, LSR-based classifier still suffers from the following issues. One is that it focuses only on the dependency between the input data and the output targets, while overlooking the local structure of instances. Another one is that using binary labels as the regression targets is too strict to fully exploit the discriminative information of the data. To address these issues, we propose a novel multiclass classification method called discriminative latent subspace learning with adaptive metric learning (DLSAML). Specifically, DLSAML adaptively learns a metric matrix for the residuals between inputs and outputs, driving smaller distances between instances of the same class and larger distances between instances of different classes in the output space. To solve the second problem, latent representations are learnt guided by the pairwise label relations as the regression targets, allowing for more flexible use of discriminative information in the data. As a combination of these two techniques, the interactive optimization of the projection matrix and metric matrix allows DLSAML to fully exploit the structural and supervised information of the data to obtain a more discriminative latent subspace for multiclass classification. Extensive experiments on several benchmark datasets have demonstrated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The requirements phase is the core of software development, if it is not carried out correctly it can cause its failure. To combat this problem, analysts have used requirements engineering (ER, for its acronym in English), which is characterized by producing a list of quality requirements called requirements specification (RS, for its acronym in English). The SR performs the requirements classification activity, which consists of identifying the class to which each requirement belongs so that analysts face the challenge of classifying them properly. This work is focused on improving the performance of the classification of non-functional requirements (NFR); that is, with the help of a convolutional neural network. It also seeks to show the importance of preprocessing, the implementation of sampling strategies, and the use of previously trained matrices such as Fasttext, Glove, and Word2vec. The results were obtained by evaluating the metrics Recall, Precision, and F1 with an average increase of up to 30% over related work. Finally, the evaluation of the model is presented with respect to the pre-trained matrices with the ANOVA analysis. [ABSTRACT FROM AUTHOR]

Spoofing detection in automatic speaker verification (ASV) systems is typically handled as a binary classification approach. In this paper, we propose a novel approach to address this problem using a multi-class classification approach. Each audio sample is tagged on the basis of the source of the signal. Spoof class samples are divided according to corresponding recording devices which were used during recording of the genuine speaker's voice to be later used for implementing playback attack. Three different multiclass based approaches proposed in this work are evaluated on ASVspoof 2017 v2.0 dataset. The performance of these systems is tested on conventional and deep classifier systems using both handcrafted features and spectrographic representations of audio. Results suggest the potential of the proposed multiclass classification based approach in comparison to binary classification, specifically in deep learning scenario. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a chronic complication of diabetes that damages the retinal blood vessels, leading to impaired vision and even blindness, and is one of the top three eye diseases causing human blindness. An effective DR grading algorithm can help ophthalmologists to diagnose patients and improve efficiency. Therefore, we propose a fusion network based on transformer and convolutional neural network (CNN) to perform DR grading. The proposed approach addresses two critical issues in the task: i) The CNN-based DR classification method has a small receptive field, so the range of available information is limited. The Transformer-based DR classification method has a large receptive field, but it is easy to lose local details; ii) Existing approaches treat DR grading as a traditional multiclass classification task, which ignores ordinal information between DR of different levels. To address i), we fuse the multi-level features of CNN and Transformer at different stages, and realize the enhancement and interaction of local and global information through the fusion module. To address ii), we propose a KC loss by formulating DR grading as a joint ordinal regression and multiclass classification problem, and obtain both category-supervised information and ordinal supervised information. Extensive qualitative and quantitative assessments have shown that our approach achieves superior performance on publicly available DeepDR and IDRiD datasets. [ABSTRACT FROM AUTHOR]

Psychological well-being is a multidimensional construct and identifying it using a systematic, comprehensive approach offers insights fundamental to critical outcomes. Social networks are valuable resources for research, providing a pragmatic way of generating empirical evidence on psychological well-being based on the textual indicators across different populations. This study analyzed the information on various Reddit social media groups dedicated to mental health. The classes, namely depression, anxiety, bipolar, and SuicideWatch, using the SWMH dataset have been analyzed. The text-based interactions of persons with mental illness have common motifs like negative language and expressions like 'hopelessness,' 'emptiness,' or 'helplessness.' Topic modeling identified recurring themes and subjects that helped classify discernible factors influencing mental health. Classifiers for multiclass classification to classify targeted mental health issues based on users' network behavior and posts were trained and tested to get predictions on context (e.g., MentalBERT) and non-context-based (e.g., LR and NB) models. The MentalBERT model outperformed the other eight baseline models with an average accuracy of 76.70%, which is 4% more than reported in previous studies. Explainable AI was used to examine the trustworthiness of each model, and the explanations were evaluated using the LIME model. Explainability is crucial as mental health data characterizes syndromes, outcomes, disorders, and signs/symptoms exhibiting probabilistic interrelationships with each other. Explanations of these intricate interconnections can assist the extensive research around the model of well-being and interventions intended to improve the human condition and distill positive human functioning. [ABSTRACT FROM AUTHOR]

Two questions often arise in the field of the ensemble in multiclass classification problems, (i) how to combine base classifiers and (ii) how to design possible binary classifiers. Error-correcting output codes (ECOC) methods answer these questions, but they focused on only the general goodness of the classifier. The main purpose of our research was to strengthen the bottleneck of the ensemble method, i.e., to minimize the largest values of two types of error ratios in the deep neural network-based classifier. The research was theoretical and experimental, the proposed Min–Max ECOC method suggests a theoretically proven optimal solution, which was verified by experiments on image datasets. The optimal solution was based on the maximization of the lowest value in the Hamming matrix coming from the ECOC matrix. The largest ECOC matrix, the so-called full matrix is always a Min–Max ECOC matrix, but smaller matrices generally do not reach the optimal Hamming distance value, and a recursive construction algorithm was proposed to get closer to it. It is not easy to calculate optimal values for large ECOC matrices, but an interval with upper and lower limits was constructed by two theorems, and they were proved. Convolutional Neural Networks with Min–Max ECOC matrix were tested on four real datasets and compared with OVA (one versus all) and variants of ECOC methods in terms of known and two new indicators. The experimental results show that the suggested method surpasses the others, thus our method is promising in the ensemble learning literature. [ABSTRACT FROM AUTHOR]

Data used in particle physics analyses have an imbalanced nature in which the events of interest are rare due to the broad background. These events can be identified from bulk by intensive computational studies including application of sophisticated analysis techniques. Classification algorithms provided by supervised machine learning (ML) approaches can be utilized to interpret skewed particle dataset as an alternative to the classic techniques even for multi particle state analysis. In this study, the ground state of the bottomonium (Υ (1 S)) and its excited states (Υ (2 S) and Υ (3 S)) were studied by application of multiclass classification approach based on random forest classifier (RFC) which is a novel ML approach example in particle analysis with implementation of resampling techniques for preprocessing dataset and modification of the weighting strategy. For this purpose, five widely used oversampling and two hybrid strategies, using over and under resampling together, were adjusted to RFC. Moreover, class weights applied RFC, weighted random forest (WRF), was used in the analysis. Due to the data structure, performance of the applied models was evaluated by the derivatives of confusion matrix. It is revealed that hybrid techniques implemented in RFC is suitable for handling highly imbalanced classes. G-mean and BAcc scores of upsilon states presented that with SMOTETomek strategy the model exhibited highest classification achievement, around 90 % , with high sensitivity implying the success of the application on multiclass classification. [ABSTRACT FROM AUTHOR]

Automatic skin lesion classification in dermoscopic images is a very challenging task due to the huge intraclass variation, the high degree of interclass visual similarity, low contrast between skin lesion and surrounding normal skin, and the existence of extraneous and intrinsic artifacts. However, existing algorithms for skin lesion classification are developed by leveraging convolutional neural networks (CNNs), and the effectiveness of these algorithms is mostly validated for binary classification of skin lesions. In addition, the relatively low diagnostic sensitivity achieved by these studies demonstrates the uncertainty involved in skin lesion classification. In order to overcome these difficulties, a swin transformer model for multiclass skin lesion classification is proposed by taking advantage of both transformer and CNNs that are based on end-to-end mapping and do not require prior knowledge. Furthermore, the problem of class imbalance is addressed through a weighted cross entropy loss. Moreover, key components of the proposed approach are explored in detail in order to ensure efficient and effective learning process with multiclass data in the skin lesion classification. The proposed method is extensively evaluated on International Skin Imaging Collaboration (ISIC) 2019 Skin Lesion Analysis Towards Melanoma Detection Challenge dataset and achieves a sensitivity, specificity, accuracy, and balanced accuracy value of 82.3 % , 97.9 % , 97.2 % , and 82.3 % , respectively. Experimental results demonstrate that the proposed method has the highest balanced accuracy value and outperforms most of the other state-of-the-art methods in multiclass skin lesion classification. [ABSTRACT FROM AUTHOR]

Despite the fact that there are numerous methods for detecting IoT intrusions, this research explorations conducted the implementation of the Top 10 Artificial Intelligence—Deep neural networks may be advantageous for unsupervised as well as supervised learning concerning IoT network traffic data. It shows a thorough comparison study to detect IoT intrusions on intelligent embedded devices which are essential to detect such intrusions using the most recent dataset IoT-23. Although several solutions are being developed to secure IoT networks, development might still be required. The use of different deep learning techniques may enhance IoT security. To enhance the security execution of IoT network traffic, the top 10 deep-learning approaches were investigated using the realistic IoT-23 dataset. For recognizing five different IoT attack classes—DoS (Denial of Service), Mirai, Scan, Normal records, and MITM-ARP (Man in the Middle attack)—we developed a variety of neural network models. In deep-learning neural network models, a "softmax" function of multiclass classification may be used to identify these assaults. NumPy, Pandas, Scikit-learn, Scipy, TensorFlow 2.2, Seaborn, and Matplotlib were only some of the programs used in the Anaconda3 environment for this study. Healthcare, banking, finance, scientific research, and corporate organizations, as well as ideas like the Internet of Things, are just some of the many fields that have embraced the utilization of AI-deep learning models. We discovered that the best deep-learning algorithms are capable of minimizing function loss, improving accuracy, as well as reducing execution time for developing that particular model. By using cutting-edge technology like deep learning neural networks as well as artificial intelligence, it makes a significant contribution to the identification of IoT anomalies. As a result, it will be effective to reduce attacks on IoT organizations. CNN (Convolutional neural networks), GANs (generative adversarial networks), and multilayer perceptron provide the best accuracy scores of 0.996317, 0.995829, and 0.996157 among the top 10 neural networks, respectively, with the smallest loss function and the shortest execution times. This paper helped to fully understand the peculiarities of IoT anomaly detection. To help you better understand various neural network models and IoT anomaly detection, this study analysis shows the Top 10 AI-deep learning model implementations. [ABSTRACT FROM AUTHOR]

In the past couple of years, location-sensitive information retrieval has gained significant attention in terms of extracting and utilizing location information present in the unstructured text. It requires analysis of documents both geographically and thematically that makes it a challenging task. The semantics of text needs to be associated with location features present in the text. Such information association is beneficial in conducting fine-grained analysis of events reported in the text, e.g., Tourist location recommendation, Disaster surveillance, Political activeness and Happiness index, etc. Recently, context-based vector space models have attained much importance in text mining as they intelligently preserve semantics of the text while representing text in vector space of desired dimension. In this paper, a framework for multiclass supervised classification of location-sensitive events, namely, LDoc2Vec is proposed that integrates context-based vector space models with geographic scope resolution of events reported in the text documents. Variants of the Doc2Vec model have been integrated with location features and their performance for multiclass supervised event classification is analysed. Experimental results with various machine learning classifiers indicate that the proposed framework outperforms baseline Doc2Vec models for multiclass classification of location-sensitive events as expressed by renowned performance measurement metrics viz. precision, recall and F1-score. [ABSTRACT FROM AUTHOR]

Chest radiography (X-ray) is the most common diagnostic method for pulmonary disorders. A trained radiologist is required for interpreting the radiographs. But sometimes, even experienced radiologists can misinterpret the findings. This leads to the need for computer-aided detection diagnosis. For decades, researchers were automatically detecting pulmonary disorders using the traditional computer vision (CV) methods. Now the availability of large annotated datasets and computing hardware has made it possible for deep learning to dominate the area. It is now the modus operandi for feature extraction, segmentation, detection, and classification tasks in medical imaging analysis. This paper focuses on the research conducted using chest X-rays for the lung segmentation and detection/classification of pulmonary disorders on publicly available datasets. The studies performed using the Generative Adversarial Network (GAN) models for segmentation and classification on chest X-rays are also included in this study. GAN has gained the interest of the CV community as it can help with medical data scarcity. In this study, we have also included the research conducted before the popularity of deep learning models to have a clear picture of the field. Many surveys have been published, but none of them is dedicated to chest X-rays. This study will help the readers to know about the existing techniques, approaches, and their significance. [ABSTRACT FROM AUTHOR]

Multiclass classification is a fundamental and challenging task in machine learning. The existing techniques of multiclass classification can be categorized as (1) decomposition into binary (2) extension from binary and (3) hierarchical classification. Decomposing multiclass classification into a set of binary classifications that can be efficiently solved by using binary classifiers, called class binarization, which is a popular technique for multiclass classification. Neuroevolution, a general and powerful technique for evolving the structure and weights of neural networks, has been successfully applied to binary classification. In this paper, we apply class binarization techniques to a neuroevolution algorithm, NeuroEvolution of Augmenting Topologies (NEAT), that are used to generate neural networks for multiclass classification. We propose a new method that applies Error-Correcting Output Codes (ECOC) to design the class binarization strategies on the neuroevolution for multiclass classification. The ECOC strategies are compared with the class binarization strategies of One-vs-One and One-vs-All on three well-known datasets of Digit, Satellite, and Ecoli. We analyse their performance from four aspects of multiclass classification degradation, accuracy, evolutionary efficiency, and robustness. The results show that the NEAT with ECOC performs high accuracy with low variance. Specifically, it shows significant benefits in a flexible number of binary classifiers and strong robustness. [ABSTRACT FROM AUTHOR]

E-sports live streaming video is rapidly coming into people's lives. High-quality video is an essential factor affecting users' perception. This paper presents conventional network traffic analysis methods for traffic intensity selection as a feature combined with deep learning classifiers for streaming videos classification with different resolutions and frame rates per second. According to the experimental results, the convolution neural networks showed the best results in multiclass classification with accuracy as high as 97%. This superiority can help E-sports operators to improve the quality of live streaming videos and provide differentiated services for their users. Furthermore, the article describes research on the performance of various deep learning classifiers with different hyperparameters. The number of filters in convolution layers and training batch size can significantly affect classification performance according to testing results. It is still necessary to avoid hyperparameters' designated values significantly influencing the classification results. [ABSTRACT FROM AUTHOR]

In the recent past, several popular failures of black box AI systems and regulatory requirements have increased the research interest in explainable and interpretable machine learning. Among the different available approaches of model explanation, partial dependence plots (PDP) represent one of the most famous methods for model-agnostic assessment of a feature's effect on the model response. Although PDPs are commonly used and easy to apply they only provide a simplified view on the model and thus risk to be misleading. Relying on a model interpretation given by a PDP can be of dramatic consequences in an application area such as forensics where decisions may directly affect people's life. For this reason in this paper the degree of model explainability is investigated on a popular real-world data set from the field of forensics: the glass identification database. By means of this example the paper aims to illustrate two important aspects of machine learning model development from the practical point of view in the context of forensics: (1) the importance of a proper process for model selection, hyperparameter tuning and validation as well as (2) the careful used of explainable artificial intelligence. For this purpose, the concept of explainability is extended to multiclass classification problems as e.g. given by the glass data. [ABSTRACT FROM AUTHOR]

Many Error-Correcting Output Codes (ECOC) algorithms had been proposed based on the hard coding (HC) schemes: binary coding {1, 0} or ternary coding {+ 1, -1, 0}. This paper introduces two novel strategies to recode the original code matrices with the mean values and the intervals of learners' outputs, which are named Mean Value Recoding (MVR) and Interval Recoding (IR) strategies. Both strategies are designed to reduce the distance between the outputs of base learners and the target codewords, aiming to produce more accurate results compared with the HC schemes. It is the first time that two concepts, soft recoding, and learner dependent, are injected into the ECOC framework to the best of our knowledge. To verify the effectiveness of our strategies, four data-independent ECOC algorithms and two data-dependent ECOC algorithms are deployed in the experiments based on UCI data sets. The experiments are carried out using the original HC strategies and our soft recoding strategies, and results verify that our strategies outperform the HC-based algorithms in most cases by producing balanced results among classes. In short, our strategies can improve the performance of different ECOC algorithms. Our python code and the corresponding data sets are available for non-commercial or research use at: https://github.com/MLDMXM2017/softcoding-ECOC. [ABSTRACT FROM AUTHOR]

Least square regression has been widely used in pattern classification, due to the compact form and efficient solution. However, two main issues limit its performance for solving the multiclass classification problems. The first one is that employing the hard discrete labels as the regression targets is inappropriate for multiclass classification. The second one is that it focus only on exactly fitting the instances to the target matrix while ignoring the within-class similarity of the instances, resulting in overfitting. To address this issues, we propose a discriminative least squares regression for multiclass classification based on within-class scatter minimization (WCSDLSR). Specifically, a ε-dragging technique is first introduced to relax the hard discrete labels into the slack soft labels, which enlarges the between-class margin for the soft labels as much as possible. The within-class scatter for the soft labels is then constructed as a regularization term to make the transformed instances of the same class closer to each other. These factors ensure WCSDLSR can learn a more compact and discriminative transformation for classification, thus avoiding the overfitting problems. Furthermore, the proposed WCSDLSR can obtain a closed-form solution in each iteration with the lower computational costs. Experimental results on the benchmark datasets demonstrate that the proposed WCSDLSR achieves the better classification performance with the lower computational costs. [ABSTRACT FROM AUTHOR]

Univariate decision tree induction methods for multiclass classification problems such as CART, C4.5 and ID3 continue to be very popular in the context of machine learning due to their major benefit of being easy to interpret. However, as these trees only consider a single attribute per node, they often get quite large which lowers their explanatory value. Oblique decision tree building algorithms, which divide the feature space by multidimensional hyperplanes, often produce much smaller trees but the individual splits are hard to interpret. Moreover, the effort of finding optimal oblique splits is very high such that heuristics have to be applied to determine local optimal solutions. In this work, we introduce an effective branch and bound procedure to determine global optimal bivariate oblique splits for concave impurity measures. Decision trees based on these bivariate oblique splits remain fairly interpretable due to the restriction to two attributes per split. The resulting trees are significantly smaller and more accurate than their univariate counterparts due to their ability of adapting better to the underlying data and capturing interactions of attribute pairs. Moreover, our evaluation shows that our algorithm even outperforms algorithms based on heuristically obtained multivariate oblique splits despite the fact that we are focusing on two attributes only. [ABSTRACT FROM AUTHOR]

In order to study and prevent water hazards in coal mines under thick loose strata, we need to make correct assessments of their water abundance levels based on the limited borehole data. According to the multi-source information composite principle, five main influencing factors of water abundance are chosen, and they are: the aquifer thickness, the thickness ratio between sandy and clayey layers, the consumption of the drilling fluid, the core recovery rate, and the hydraulic conductivity. Their spatial variations in the whole study area could be inferred from Kriging interpolation. Next, we have developed a novel off-the-shelf two-step assessment approach. In the first step, we apply a dimensionality reduction technique called Fisher discriminant analysis (FDA) to project the original normalized data into a low-dimensional space, which is convenient for data visualization. In this projection process, we want to keep the original information as much as possible. In the second step, we train three classification algorithms on the same transformed low-dimensional data to predict the water abundance level, and leave-one-out cross-validation is used to validate our proposed method due to data sparsity. Finally, the comprehensive zoning map of the study area's water abundance level is established, which can provide scientific guidance for the mining operations and prevention of mine water hazards in this region. The whole process is further elaborated through a case study of the Baodian coal mine, from which we are able to know the location with the highest water abundance level. [ABSTRACT FROM AUTHOR]

This paper compares the classification and prediction capabilities of decision tree (DT), genetic programming (GP), and gradient boosting decision tree (GBT) techniques for one-month ahead prediction of standardized precipitation index in Ankara province and standardized precipitation evaporation index in central Antalya region. The evolved models were developed based on multi-station prediction scenarios in which observed (reanalyzed) data from nearby stations (grid points) were used to predict drought conditions in a target location. To tackle the rare occurrence of extreme dry/wet conditions, the drought series at the target location was categorized into three classes of wet, normal, and dry events. The new models were trained and validated using the first 70% and last 30% of the datasets, respectively. The results demonstrated the promising performance of GBT for meteorological drought classification. It provides better performance than DT and GP in Ankara; however, GP predictions for Antalya were more accurate in the testing period. The results also exhibited that the proposed GP model with a scaled sigmoid function at root can effortlessly classify and predict the number of dry, normal, and wet events in both case studies. [ABSTRACT FROM AUTHOR]

The accurate mapping of lithological units in tropical environments characterised by dense forest, persistent cloud cover, and limited bedrock exposures from remote sensing data is very challenging. In this paper, we propose the design and implementation of an Automatic Deep Stacked Sparsely Connected Convolutional Autoencoder (ADSSCCA) neural network for lithological mapping without data filtering. The model integrates convolutional neural networks architecture with Principal Component Analysis for feature extraction and dimensionality reduction. We also combine 3D and 2D convolutions to generate feature maps, and the fully connected block is configured using the ADSSCA philosophy with dropout calculated as a function of model capacity. The dropout serves to randomly drop hidden computing units and their connections during training, thus reducing overfitting and improving the model’s generalization ability. The proposed classification model was tested on non-processed Landsat-8 images from the Yaounde-Sangmelima region in southern Cameroon. The proposed ADSSCCA mapped the five dominant lithological classes namely, gneisses, schists, micaschists, biotite-granites and pyroxene-granites with an impressive overall accuracy of 99.85%. The novelty in this study is the removal of the pre-processing stage which results in a simplified work flow, reduces overhead and preserves data integrity. Secondly, the introduction of dynamically calculated dropout based on neuron and layer count enhances generalization. It is expected that; this new finding will enhance lithological mapping and analysis in the forest zones and areas with similar characteristics. [ABSTRACT FROM AUTHOR]

We introduce the idea that using optimal classification trees (OCTs) and optimal classification trees with-hyperplanes (OCT-Hs), interpretable machine learning algorithms developed by Bertsimas and Dunn (Mach Learn 106(7):1039–1082, 2017), we are able to obtain insight on the strategy behind the optimal solution in continuous and mixed-integer convex optimization problem as a function of key parameters that affect the problem. In this way, optimization is not a black box anymore. Instead, we redefine optimization as a multiclass classification problem where the predictor gives insights on the logic behind the optimal solution. In other words, OCTs and OCT-Hs give optimization a voice. We show on several realistic examples that the accuracy behind our method is in the 90–100% range, while even when the predictions are not correct, the degree of suboptimality or infeasibility is very low. We compare optimal strategy predictions of OCTs and OCT-Hs and feedforward neural networks (NNs) and conclude that the performance of OCT-Hs and NNs is comparable. OCTs are somewhat weaker but often competitive. Therefore, our approach provides a novel insightful understanding of optimal strategies to solve a broad class of continuous and mixed-integer optimization problems. [ABSTRACT FROM AUTHOR]

One-against-one and one-against-all are common approaches to break down multiclass classification problems into binary classification problems and build a multiclass classifier. The former approach often yields better multiclass classifiers than the latter due to its structure. The one-against-all approach strengthens or sometimes creates linear inseparability and class imbalance in the binary classifiers during the training phase. In this sense, balancing techniques can be applied to handle the binary imbalance problem and motivate the use of the computationally simpler approach. The one-against-all approach with balancing techniques proposed in this work reaches better accuracy values than the pure one-against-all approach for 7 out of 8 datasets and shows a considerable increase in the weighted recall value for 4 out of 8 datasets. Besides, the accuracy values of the one-against-all approach with balancing techniques are considerably closer to the ones found by the one-against-one approach with less computational efforts. [ABSTRACT FROM AUTHOR]

A fundamental problem of supervised learning algorithms for brain imaging applications is that the number of features far exceeds the number of subjects. In this paper, we propose a combined feature selection and extraction approach for multiclass problems. This method starts with a bagging procedure which calculates the sign consistency of the multivariate analysis (MVA) projection matrix feature-wise to determine the relevance of each feature. This relevance measure provides a parsimonious matrix, which is combined with a hypothesis test to automatically determine the number of selected features. Then, a novel MVA regularized with the sign and magnitude consistency of the features is used to generate a reduced set of summary components providing a compact data description. We evaluated the proposed method with two multiclass brain imaging problems: 1) the classification of the elderly subjects in four classes (cognitively normal, stable mild cognitive impairment (MCI), MCI converting to AD in 3 years, and Alzheimer's disease) based on structural brain imaging data from the ADNI cohort; 2) the classification of children in 3 classes (typically developing, and 2 types of Attention Deficit/Hyperactivity Disorder (ADHD)) based on functional connectivity. Experimental results confirmed that each brain image (defined by 29.852 features in the ADNI database and 61.425 in the ADHD) could be represented with only 30 − 45% of the original features. Furthermore, this information could be redefined into two or three summary components, providing not only a gain of interpretability but also classification rate improvements when compared to state-of-art reference methods. [ABSTRACT FROM AUTHOR]

This paper presents a new method that can classify multiple motor imageries and can be implemented in a realistic application because of its low computation time. The method proposes the use of pattern images, generated with the common spatial pattern (CSP) technique. The paper also suggests a new algorithm to determine the best frequency bands for optimal discrimination among the diverse motor imageries to classify. The pattern images and the state images, which represent the mental state of the user in a specific segment of time, are used to compute cross-correlation coefficients. Feature vectors, including characteristics obtained with CSP, and the mean and variance of the correlation coefficients were employed to design binary classifiers with support vector machines. In addition, the work includes a real-time simulation involving a sliding window technique. The proposed method was evaluated in four datasets: IVa, IVb and V from BCI Competition III and another provided by the software BCILAB, which compared with other state-of-the-art methods. The results that overcome surpassed the methods in these competitions and other state-of-the-art methods mentioned in this paper. The method also presents short computation time, robustness between subjects and capability to classify between multiple mental states. [ABSTRACT FROM AUTHOR]

The objective of the proposed research is to classify electroencephalography (EEG) data of covert speech words. Six subjects were asked to perform covert speech tasks i.e mental repetition of four different words i.e 'left', 'right', 'up' and 'down'. Fifty trials for each word recorded for every subject. Kernel-based Extreme Learning Machine (kernel ELM) was used for multiclass and binary classification of EEG signals of covert speech words. We achieved a maximum multiclass and binary classification accuracy of (49.77%) and (85.57%) respectively. The kernel ELM achieves significantly higher accuracy compared to some of the most commonly used classification algorithms in Brain–Computer Interfaces (BCIs). Our findings suggested that covert speech EEG signals could be successfully classified using kernel ELM. This research involving the classification of covert speech words potentially leading to real-time silent speech BCI research. [ABSTRACT FROM AUTHOR]

An efficient and robust algorithm based on mixed integer linear programming is proposed to extend the Logical Analysis of Data (LAD) methodology to solve multiclass classification problems, where One-vs-Rest learning models are constructed to classify observations in predefined classes. The proposed algorithm uses two control parameters, homogeneity and prevalence, for identifying relaxed (fuzzy) patterns in multiclass datasets. The utility of the proposed method is demonstrated through experiments on multiclass benchmark datasets. Numerical experiments show that the efficiency and performance of the proposed multiclass LAD method with relaxed patterns is comparable to, if not better than, those of the previously developed LAD based multiclass classification as well as other well-known supervised learning methods. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) learns the maximum margin to separate training examples that belong to two classes and has been widely used in many pattern recognition tasks due to its high effectiveness. However, conventional SVM suffers from the following deficiencies: (1) SVM cannot take full advantage of multiple modalities in a dataset if they are available, and (2) SVM trains the marginal hyper-plane by solving the quadratic programming problem, and thus costs too much computational overheads. In this paper, we propose a correlation maximization machine (CMM) model to overcome the aforementioned deficiencies by integrating two modalities in a dataset to boost the classification performance and utilizing randomized nonlinear features to output labels from multiple classes. In particular, CMM reveals the nonlinear relationships among both modalities by generating randomized nonlinear features for each modality. CMM learns to project these features into a common subspace with a constraint that their coefficients are highly correlated, and narrows the gap between the coefficients of both modalities and the class indicator of each training example to deal with multiclass classification problem. At the classification stage, CMM indicates the classes of a test example by using the summation of its coefficients of both modalities. Since the objective function of CMM is non-convex, it is quite difficult to obtain the global minimum. In this paper, we developed a block coordinate descent-based algorithm to optimize CMM and theoretically proved its convergence to a local minimum. Experimental results of face recognition on three popular face image datasets and experimental results of image retrieval on CIFAR-10, NUS-Wide, and Wikipedia datasets demonstrate that CMM outperforms the representative methods. [ABSTRACT FROM AUTHOR]

Using binary classification techniques to perform multi-class classification of data is still of great practical interest due to the robustness and simplicity of binary classifiers. These techniques produce a single multi-class classification decision based on many binary decisions. Our work relies on the simple observation that as dimensionality increases so does the data sparsity and, consequently, a single binary classifier may separate multiple classes. Therefore, we claim that the number of binary decisions can be significantly reduced. We present Divide and Conquer Support Vector Machines (DCSVM), an efficient algorithm for multi-class classification using Support Vector Machines. DCSVM is a divide and conquer algorithm which relies on data sparsity in high dimensional space and performs a smart partitioning of the whole training data set into disjoint subsets that are easily separable. A single prediction performed between two partitions eliminates at once one or more classes in one partition, leaving only a reduced number of candidate classes for subsequent steps. The algorithm continues recursively, reducing the number of classes at each step, until a final binary decision is made between the last two classes left in the competition. In the best case scenario, our algorithm makes a final decision between k classes in O (log k) decision steps and in the worst case scenario DCSVM makes a final decision in k - 1 steps, which is not worse than the existent techniques. [ABSTRACT FROM AUTHOR]

In this article we focus on the semi-supervised learning. Semi-supervised learning typically is a learning task from both labeled and unlabeled data. We especially consider the multiclass semi-supervised classification problem. To solve the multiclass semi-supervised classification problem we propose a new multiclass loss function using new codewords. In the proposed loss function, we combine the classifier predictions, based on the labeled data, and the pairwise similarity between labeled and unlabeled examples. The main goal of the proposed loss function is to minimize the inconsistency between classifier predictions and the pairwise similarity. The proposed loss function consists of two terms. The first term is the multiclass margin cost of the labeled data and the second term is a regularization term on unlabeled data. The regularization term is used to minimize the cost of pseudo-margin on unlabeled data. We then derive a new multiclass boosting algorithm from the proposed risk function, called GMSB. The derived algorithm also uses a set optimal similarity functions for a given dataset. The results of our experiments on a number of UCI and real-world biological, text, and image datasets show that GMSB outperforms the state-of-the-art boosting methods to multiclass semi-supervised learning. [ABSTRACT FROM AUTHOR]

After Minsky and Papert (Perceptrons, MIT Press, Cambridge, 1969) showed the inability of perceptrons in solving nonlinearly separable problems, for several decades people misinterpreted it as an inherent weakness that is common to all single-layer neural networks. The introduction of the backpropagation algorithm reinforced this misinterpretation as its success in solving nonlinearly separable problems passed through the training of multilayer neural networks. Recently, Conaway and Kurtz (Neural Comput 29(3):861–866, 2017) proposed a single-layer network in which the number of output units for each class is the same as input units and showed that it could solve some nonlinearly separable problems. They used the MSE (Mean Square Error) between the input units and the output units of the actual class as the objective function for training the network. They showed that their method could solve the XOR and M&S'81 problems, but it could not do any better than random guessing on the 3-bit parity problem. In this paper, we use a soft competitive approach to generalize the CE (Cross-Entropy) loss, which is a widely accepted criterion for multiclass classification, to networks that have several output units for each class, calling the resulting measure the CCE (Competitive cross-entropy) loss. In contrast to Conaway and Kurtz (2017), in our method, the number of output units for each class can be chosen arbitrarily. We show that the proposed method can successfully solve the 3-bit parity problem, in addition to the XOR and M&S'81 problems. Furthermore, we perform experiments on several datasets for multiclass classification, comparing a single-layer network trained with the proposed CCE loss against LVQ, linear SVM, a single-layer network trained with the CE loss, and the method of Conaway and Kurtz (2017). The results show that the CCE loss performs remarkably better than existing algorithms for training single-layer neural networks. [ABSTRACT FROM AUTHOR]

Automatic plant species identification is one of the recent and fascinating research area as plants are crucial element of ecosystem. Several plant species exist with significant importance but most of us are unaware of the diversity of plant species available on earth. Their utility to humans starts as oxygen provider, food source, and medicinal compounds essential for medicines that are difficult to develop in right proportions. Being the first living habitants of earth, they have roots far deeper in the ecosystem than any living being. Hence, it is utmost important to develop automatic plant species identification system in which the digital image of the plant is given as input and the label of the plant is determined by the system. In this paper, we have focused on three different aspects (i) Significance of threshold (ii) Feature descriptor that can best describe the leaf images and (iii) Proposed a novel classification method called Multi class Twin Support Vector Machine which in an extension of widely used Twin Support Vector Machine classifier. The performance of the proposed method is compared with SVM, Multi Birth SVM and Probabilistic Neural Network. It is observed that the proposed classifier outperforms all the aforementioned classifiers on publicly available datasets. [ABSTRACT FROM AUTHOR]

A new type of classifier, the lightweight nearest convex hull (LNCH) classifier, is proposed. It is called lightweight due to the simplicity of its algorithm. It is based on a new method for estimating the proximity of the test point to the convex hull of a class in the case when the test point intersects convex hulls of the classes. The concept of the penetration depth of a point into a convex hull is used. Proximity is determined based on the analysis of extreme points projected on the direction vector from this point to the centroid of the class. A decision rule for multiclass problems is derived for the LNCH classifier using a new method for estimating the proximity. The results of experimental studies on synthesized numerical data and on real data for breast cancer diagnosis are given. The results indicate higher recognition accuracy of the LNCH classifier compared to other types of classifiers. [ABSTRACT FROM AUTHOR]

Lots of real-world problems require multiclass classification. Since most general classification methods are originally introduced for binary problems (including two classes), they should be extended to multiclass problems. A solution proposed for multiclass problems is to decompose such problems to several binary ones and then combine the results obtained from smaller problems as a tree-based structure to obtain the final solution. In this study, a novel method which uses VlseKriterijumska optimizacija I Kompromisno Resenje multi-criteria decision-making was proposed to build the best directed binary tree with minimum error. The proposed method is independent of classifier; nevertheless, in the current experiments, the support vector machine was employed as the base classifier. The proposed method was tested on datasets and the results were compared with other methods. It can be seen that it improves precision of predictions significantly. [ABSTRACT FROM AUTHOR]

Support Vector Machine (SVM) is mainly used to classify the data into two categories. To solve the multi-category problems using SVM, researchers used two approaches. The first approach based on solving multiple SVM binary classifiers, whereas another approach based on solving a single optimization problem. In this paper, we have used the first approach and proposed an Efficient Multiclass Support Vector Machine (ESVM) algorithm using a skewed binary tree. To construct the skewed binary tree, no extra efforts are required as compared to the binary tree approach. The algorithm is tested on the benchmark data sets, and the results are compared with both the multiclass approaches of SVM. The ESVM’s results are compared with five techniques of solving multiple binary SVM classifiers and four techniques of solving a single optimization problem. The comparative experiments prove the efficiency of the ESVM in terms of its accuracy as compared to other contemporary algorithms. Further, ESVM is successfully applied for classification of the email dataset into positive, negative and neutral sentiments. [ABSTRACT FROM AUTHOR]

Remote sensing images (RSIs) are increasingly used as data source to produce maps used in several applications. Modern sensors launched into space from the end of the 1990s have been producing high spatial resolution RSIs. The use of classification methods based on regions, called as Geographic Object-Based Image Analysis (GEOBIA), has been demonstrated to be more appropriate to deal with this kind of image. However, finding the appropriate segmentation scale, which is not a trivial task, is crucial for the success of a GEOBIA method. In this paper, we perform a comparative study involving seven methods for RSI multiclass classification that combine different features extracted from different scales: M1-OvA, M2-OvO, M3-AdaMH, M4-Samme, M5-MV, M5-WMV, and M6-Cascade. The first four methods are boosting-based techniques and the last three are based on the majority vote approach. The effectiveness of the proposed methods was evaluated by analyzing the results of experiments conducted in three RSIs datasets. The methods were compared with the baseline SVM with Kernel RBF by measuring the overall accuracy, the Kappa Index, and the accuracy per class. The results show that all the proposed methods are effective for RSI classification. [ABSTRACT FROM AUTHOR]

Confronting with plenty of information from multiple targets and multiple sources, it is core and important to learn and decide for the bionic brain or robot within limited capacity in the bionic-technology field. Several multiclass multiple kernel learning algorithms are proposed instead of a single one, which can not only combine multiple kernels corresponding to different notions of similarity or information from multiple feature subsets, but also avoid kernel parameters selecting and fuse distinctions of multiple kernels. The core of these algorithms is the small sphere and large margin approach with hypersphere boundary, which takes the advantages of support vector machine (SVM) and support vector data description (SVDD), making the volume of sphere as small as well and the margin as large as possible, in other words, minimizing the within-class divergence like SVDD and maximizing the between-class margin like SVM. Meanwhile, The one-class essence of SSLM can relieve problem of the data imbalance. Besides, the one-against-all strategy is adopted for multiclass recognition. Hence, there will be a remarkable improvement of recognition accuracy. Numerical experiments based on three publicly UCI datasets demonstrate that using multiple kernels instead of a single one is useful and promising. These MMKL algorithms are ideal for classification and recognition of multiple targets and sources in artificial intelligence field. [ABSTRACT FROM AUTHOR]

In the recruitment domain, knowing the employer industry of jobs is important to get an insight about the demand in each industry. The existing system at CareerBuilder uses an employer name normalization system and an employer knowledge base (KB) to infer the employer industry of a job. However, errors may occur during the computation of the job employer and in the construction of the employer KB with the industry attributes. Since the KB is huge, it is not possible to manually detect the errors. Therefore, in this paper we use machine learning techniques to automatically detect the errors. With the observation that the main jobs posted by an employer often relate to the employer industry, e.g., truck driver jobs often correspond to employers in the transportation industry, we develop a system that classifies the industry of an employer using job posting data. We aggregate job postings from an employer and derive features from employer names, employer descriptions, job titles, and job descriptions to predict the industry of the employer. Two models are used for classification: (1) support vector machine and (2) random forest. Our experiments show that random forest is more effective than SVM in identifying the errors in the existing industry classification system, which achieves precision 0.69, recall 0.78, and f-score 0.73. It especially better handles mixed feature vectors when normalization errors occur. We also observe that generally our models perform better in detecting errors for industries that have higher error rates. [ABSTRACT FROM AUTHOR]

This paper proposes a framework to obtain ensembles of classifiers from a Multi-objective Evolutionary Algorithm (MOEA), improving the restrictions imposed by two non-cooperative performance measures for multiclass problems: (1) the Correct Classification Rate or Accuracy (CCR) and, (2) the Minimum Sensitivity (MS) of all classes, i.e., the lowest percentage of examples correctly predicted as belonging to each class with respect to the total number of examples in the corresponding class. The proposed framework is based on collecting Pareto fronts of Artificial Neural Networks models for multiclass problems by the Memetic Pareto Evolutionary NSGA2 (MPENSGA2) algorithm, and it builds a new Pareto front (ensemble) from stored fronts. The ensemble built significantly improves the closeness to the optimum solutions and the diversity of the Pareto front. For verifying it, the performance of the new front obtained has been measured with the habitual use of weighting methodologies, such as Majority Voting, Simple Averaging and Winner Takes All. In addition to CCR and MS measures, three trade-off measures have been used to obtain the goodness of a Pareto front as a whole: Hyperarea, Laumanns’s Hyperarea (LAUMANNS) and Zitzler’s Spread (M3). The proposed framework can be adapted for any MOEA that aims to improve the compaction and diversity of its Pareto front, and whose fitness functions impose severe restrictions for multiclass problems. [ABSTRACT FROM AUTHOR]

Multiclass classification is an important task in pattern analysis since numerous algorithms have been devised to predict nominal variables with multiple levels accurately. In this paper, a novel support vector machine method for twin multiclass classification is presented. The main contribution is the use of second-order cone programming as a robust setting for twin multiclass classification, in which the training patterns are represented by ellipsoids instead of reduced convex hulls. A linear formulation is derived first, while the kernel-based method is also constructed for nonlinear classification. Experiments on benchmark multiclass datasets demonstrate the virtues in terms of predictive performance of our approach. [ABSTRACT FROM AUTHOR]