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Abstract: In this paper, we describe the development of a system for evaluating an specific quality characteristic of recycled paper sheets using techniques of image analysis and pattern recognition. We call Bumpiness the phenomenon of interest, which is new in the literature on paper quality. This phenomenon is characterized by the appearance of macroscopic undulations on the paper sheet surface that may emerge shortly or some time after its production. We explore the detection and measurement of this defect by means of computer vision and statistical pattern recognition techniques that may allow early detection at the production site. Our goal is to give an scalar continuous measure of Bumpiness. We propose features computed from Gabor filter banks (GFB) and discrete wavelet transforms (DWT) for the characterization of paper sheet surface Bumpiness in recycled paper images. The starting point is to state the problem as a classification of the paper sheet images into two classes: low and high Bumpiness. In this setting we obtain, with both proposed texture modelling approaches (GFB and DWT), classification accuracies comparable to the agreement between human observers. The best performance is obtained using DWT features. Finally, we propose as the scalar index of Bumpines the fisher discriminant analysis (FDA) function defined on the space of the best features for the classification task. We perform an innovative validation process of this Bumpiness index, based on the ordering of random pairs of images, obtaining a very high agreement with the human observers. [Copyright &y& Elsevier]

In the contemporary world, data pervades every facet of human life, and the information contained in this data plays a pivotal role in shaping decision-making and advancing technology. Among the plethora of techniques available, classification methods are highly effective tools for extracting valuable insights from vast volumes of data. The R ule A ggregation C lassifi ER (RACER) is a novel rule-based classification algorithm known for its exceptional performance. A notable limitation of RACER lies in its inability to handle continuous features. In this paper, we address the aforementioned limitation by employing various supervised discretization methods, including CAIM, MDLP, Decision Tree (CART), and ChiMerge. The impact of these methods on RACER's accuracy and understandability is evaluated across nine datasets from the UCI repository. Additionally, the paper conducts a comparative analysis of RACER's accuracy against well-known classifiers such as Naive Bayes, Logistic Regression, SVM, LightGBM, and Decision Tree. The findings indicate that RACER achieves the highest average accuracy when we utilize MDLP as the discretization method, surpassing its initial average accuracy. Moreover, RACER demonstrates superior understandability by generating the lowest number of rules when employing ChiMerge and Decision Tree for discretizing numerical features. Furthermore, RACER outperforms the other five classifiers when employing MDLP. • The discretization unit enables the RACER algorithm to handle continuous data. • The discretization unit improves the classification accuracy of the RACER. • The discretization unit increases the understandability of the RACER rules. [ABSTRACT FROM AUTHOR]

Hierarchical text classification (HTC) aims to classify a text into multiple categories organized in a hierarchical structure. The state-of-the-art HTC methods usually employ graph networks, where label graphs are constructed and label representation is learned to interact with text representations for classification. In general, label graphs are built on the intrinsic label hierarchy, label semantic similarity, or label co-occurrence. Such graphs have been proven to be effective, but they only exploit knowledge from training data or simple label descriptions, without considering the vast external knowledge in the open sources. Actually, external knowledge from open sources could bring in complementary information to enhance the label graph's representation power. Motivated by the above considerations, we explore the use of external knowledge for improving HTC in this paper. We categorize knowledge into micro-knowledge and macro-knowledge, which are defined as the fundamental concepts related to a single class label and the correlations among class labels, respectively. For tailor-made incorporation of the two types of knowledge into representation learning and classification, we propose Adaptive Micro- and Macro-Knowledge Incorporation for Hierarchical Text Classification (AMKI-HTC) model in this paper. The micro-knowledge incorporation helps capture class-relevant keywords in the text and hence produce discriminative representations, while the macro-knowledge incorporation improves the accuracy of label graphs. Finally, a confidence maximization fusion strategy is developed for adaptive aggregation of multi-view features. Extensive experiments on three benchmark HTC datasets demonstrate that AMKI-HTC consistently outperforms state-of-the-art models. • Conceptualize extensive external knowledge as micro-knowledge and macro-knowledge. • Aggregate micro- and macro-knowledge into original text features adaptively. • Construct text-knowledge graphs to emphasize discriminant information. • Simplify hierarchy-GCN as SH-GCN with improved performance. • Adaptively fuse features from multiple label graphs by confidence maximization. [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]

This paper is dedicated to solving the problem of Advanced Persistent Threat (APT) attack and defense in the Industrial Internet of Things (IIoT). Due to the diversity of IIoT equipment and the inconsistency of protection capabilities, it is difficult for the existing uniform defense strategy and the random defense strategy to achieve ideal results. Considering that both attackers and defenders aim to achieve maximum benefits by paying the minimum cost, as well as the differences between devices, this paper proposes an equipment classification based differential game method for APT in IIoT. Firstly, all equipment is divided into two categories according to their protective capabilities. Secondly, the APT attack and defense process is mathematically described, and the corresponding differential game problem is formulated and analyzed theoretically. Finally, the theoretical results of this method are verified by various experiments, including the comparisons with the uniform defense strategy, the random defense strategy, and the latest model. • A new equipment-based evolutionary dynamics model is proposed. • A necessity system is established to narrow the scope of searching for Nash equilibrium. • An algorithm is designed to generate the Nash equilibrium. [ABSTRACT FROM AUTHOR]

In this paper, motivate by quantile in the field statistics and bounded linex loss function a novel robust bounded quantile loss is proposed for improving the performance of support vector machine (SVM) and support vector regression (SVR). The bounded quantile loss has some important properties such as asymmetry, non-convexity, which make SVM and SVR based on bounded quantile (BQ-SVM and BQ-SVR) robust to noise. The Fisher consistency and generalization error bound of BQ-SVM guarantees the generalization capability of BQ-SVM. In addition, we derive the influence functions of BQ-SVM and BQ-SVR to show that they are less sensitive to outliers. However, the non-convexity of the proposed bounded quantile makes it difficult to optimize. We utilize the concave-convex procedure (CCCP) technique to solve the proposed models. A host of numerical studies on artificial and benchmark datasets are conducted, and various evaluation criteria were considered to demonstrate the effectiveness and the efficiency of the proposed method. Experimental results indicate that our proposed methods is more robust compare to classical and advanced methods when the dataset contains outliers in both classification and regression applications. • A novel bounded quantile loss (L b q loss) is proposed for robust learning problems. • The support vector machines based methods with L b q loss (BQ-SVM and BQ- SVR) are robust. • The designed ClipDCD-based CCCP algorithm is efficient to solve the BQ-SVM and BQ-SVR. • Numerical and theoretical results demonstrate the effectiveness of BQ-SVM and BQ-SVR. [ABSTRACT FROM AUTHOR]

Point cloud classification has emerged as a vital research area in several emerging applications, including robotics and autonomous driving. However, discriminative feature learning has long been a challenging issue owing to the irregularity and disorder of point clouds. Recently, although Transformer-based methods can achieve high accuracy in point cloud learning, plenty of Transformer layers bring huge computation and memory consumption. This paper presents a novel hierarchical local–global framework based on Transformer network for point clouds, named TNPC. TNPC contains two serial Stages implementing downsampling operation, and each Stage is composed of a local feature extracting (LFE) block and a global feature extracting (GFE) block, which can reduce computation and memory consumption obviously. LFE block consists of two parallel branches, namely Transformer branch and shared multilayer perceptron (SMLP) branch, which are designed to learn relevant feature of any two points and local high-dimensional semantic features of each point between sampling centroid and its neighborhoods, respectively. The proposed two parallel branches not only improve the feature extraction effect, but also reduce the computation and memory consumption. GFE block consists of a center point contact (CPC) module and a global point cloud transformer layer (PCTL) module, which can improve the effect of global features extracting without adding the number of parameters and computation. The performance of our method is validated experimentally on ModelNet40 and ScanObjectNN datasets. Our method improves the mean accuracy on each category (m A c c) to 91.6% and 79.8% on the ModelNet40 dataset and ScanObjectNN dataset, respectively. In terms of efficiency, our method leads to a significant reduction, with only 4.73MB parameters and only 1.91GB floating-point operations (FLOPs). Experimental results demonstrate that the proposed method achieves state-of-the-art performance on classification accuracy and efficiency. • A novel hierarchical local–global framework is proposed for 3D point clouds. • LFE block contains parallel Transformer branch and SMLP branch. • LFE block can learn local high-dimensional semantic features of each point. • GFE block is designed to extract the global feature of point cloud effectively. [ABSTRACT FROM AUTHOR]

The classification of huge complex datasets to extract the information needed for making informed decisions has gained a lot of importance in today's scenario due to the outspurt of data. With an increase in dataset size, the means of obtaining the desired information via data clustering techniques also became complex. This paper illustrates a framework for selecting the best combination of the Kernel function and the Lattice structure for better classification based on the size of the data. The performance of the SOM majorly depends on the choice of Kernel Function, Lattice Structures, and the size of the datasets. The types of kernel functions deployed in this study are Gaussian, Exponential, and Laplacian Kernel along with the lattice Structures such as hexagonal, Rectangular, and Square which result in nine combinations respectively. These nine combinations of the Kernel Function and the Lattice Structure have been tested on datasets ranging from small to large size using the performance metrics. A frequency table has been arrived at based on the classification of the dataset size, combinations of Kernel functions, and Lattice Structures. The results of this study infer that the combination of Gaussian Kernel -Square Lattice performs better for small-size datasets wherein the Exponential and Laplacian Kernel functions along with Hexagonal Lattice perform better for mid-size datasets, while the Laplacian Kernel – Square Lattice perform better for large size datasets. [ABSTRACT FROM AUTHOR]

This paper provides the first review to date which gathers, describes, and assesses, to the best of our knowledge, all available publications on automating cerebral microbleed (CMB) detection. It provides insights into the current state of the art and highlights the challenges and opportunities in this topic. By incorporating the best practices identified in this review, we established guidelines for the development of CMB detection systems. We are confident that these guidelines can serve as a foundation for further research. CMB detection is a crucial but challenging task that can be laborious for radiologists. With the increasing popularity of magnetic resonance imaging (MRI), the ability to detect CMBs has improved, but there is still a need to automate this process to enhance its efficiency and accuracy. A high prevalence of CMBs is closely associated with cognitive dysfunction, diabetes, hypertension, an increased risk of stroke, and intracerebral hemorrhage. It is alarming to note that strokes, Alzheimer's disease, and Diabetes mellitus have secured their position as the second, seventh, and ninth most common causes of death worldwide, respectively. Moreover, CMBs are sometimes found in association with other pathologies and indicate a range of pathological processes in the cerebral vessels. Thus, it is essential to enhance the quality of diagnostics to facilitate prompt identification and treatment of these potentially life-threatening conditions. In this paper, we aimed to systematize the existing knowledge and best practices in automatic CMB detection, from fundamental information about CMBs and MRI image data, through employed datasets and CMB detection and verification algorithms, to methods of result evaluation. This can serve as a starting point for future research and the development of a CMB detection system that is practically applicable in medicine, leading to enhanced patient treatment outcomes. • There is a need for automatization of laborious CMB detection process. • Automatic CMB detection suffers from high production of false-positive predictions. • The scarcity of publicly available CMB datasets hinders research comparison. [ABSTRACT FROM AUTHOR]

There are lots of research papers for ASC, and in recent years it is rapidly increasing. DCASE also provides different types of competition for the submission of several papers to solve the various tasks of ASC and it is the opportunity for the research scholars either to participate in those competitions or to provide the enhanced model for ASC. This paper provides details about the various recent approaches along with the block diagram used for pre-processing required before model development for ASC. It also includes a description of different recent techniques used for the classification of different sounds for ASC tasks. The comparative analysis for different recent available techniques both for pre-processing and classification has been done and summarized in this paper. It also describes the contributions towards the survey on ASC by comparing this paper with some existing survey papers based on several parameters like functionality described separately, results from description with quantifiable value, a dataset with proper quantifiable analysis, and pictorial representation of model discussed, etc. Finally, considering the benefits for eminent research scholars, this paper has also focused on the details for future directions for both pre-processing and classification for ASC. [Display omitted] • A systematic review on pre-processing and classification techniques for acoustic scenes. • Comparative analysis of pre-processing and classification techniques. • Discussion on different processing techniques for classification. • Identifies the future directions for further research in the area. [ABSTRACT FROM AUTHOR]

In the era of data-driven decision-making, extracting meaningful insights from vast amounts of information is paramount. In organizing this data, classification methods play a pivotal role. Among the existing classification techniques, rule-based classifiers have gained prominence for their effectiveness and interpretability. One such is the R ule A ggregation C lassifi ER (RACER), known for its exceptional performance but limited when dealing with high-dimensional, low-sample-size datasets. In this paper, we introduce the R ule OP timized A ggregation C lassifier (ROPAC) as an extension of RACER that incorporates two different rule optimization methods, resulting in ROPAC-L and ROPAC-M, which aim to improve overall performance and classification accuracy. We evaluated this algorithm through experimentation with fifty datasets from reputable sources, such as the OpenML website and the UCI Machine Learning Repository. Furthermore, the proposed algorithm's accuracy is compared with fifteen well-known classifiers. Our results demonstrate that ROPAC outperforms all the other algorithms in terms of accuracy, showcasing its superiority and dominance in various data scenarios. • Proposed ROPAC, Rule OPtimized Aggregation Classifier, a novel rule-based classifier. • Introduced ROPAC-L and ROPAC-M to optimize rules and expand search space exploration. • Evaluated ROPAC on 50 diverse datasets, comparing accuracy with 15 famous algorithms. • Confirmed ROPAC-L as the most accurate, leading classifier based on the experiments. [ABSTRACT FROM AUTHOR]

Feature selection facilitates uncertainty disposal and information mining, and it has received widespread research interests. Divergence-based fuzzy rough sets (Div-FRSs), a new kind of fuzzy rough sets, have been applied to feature selection and induced two efficient algorithms, FS-AFS and FS-RFS. Nevertheless, FS-AFS and FS-RFS still have advancement space, because the dependency functions only focus on lower approximation and ignore the uncertainty in upper approximation, which will certainly undermine the algorithmic evaluation effects. To this end, this paper introduces the upper approximation and fuses it with lower approximation via class-specific pattern and Spearman coefficient to construct a new information measurement for metric perfection, called Spearman-based self-information. Relying on this new measurement, a novel feature selection algorithm SPESI is established to improve FS-AFS and FS-RFS. At first, Spearman coefficient is introduced and upper approximation is defined. Second, class-driven precision and roughness are built by incorporating Spearman coefficient-based class-specific weight vectors with class-driven lower and upper approximations. Meanwhile, the granulation monotonicity of newly-defined measurements is also explored. Then, the core measurement Spearman-based self-information is firstly given and its feature significance motivates a feature selection algorithm SPESI with heuristic search. Finally, data experiments are implemented to validate the effectiveness of SPESI, and a conclusion can be drawn that SPESI outperforms FS-AFS and FS-RFS to acquire better classification performances with fewer feature numbers and less running time. • Upper approximation is first defined in Div-FRSs to induce measurement. • Class-specific information of D s enriches decision analysis. • Spearman correlation coefficient reveals the relevance between classes. • Algorithm SPESI outperforms FS-AFS and FS-RFS to offer better classification performances. [ABSTRACT FROM AUTHOR]

In this paper, we propose a technique for hierarchical yoga pose classification (YPC) in a multi-stage multi-tasking framework. We propose a three-stage transfer learning based end-to-end training methodology. Novelty lies in (a) proposed supervised contrastive combined loss function for stage-1 training, (b) proposed Encoder–Decoder network architecture with attention mechanism for stage-3 training, (c) proposed spatial context aware multi-tasking combined loss function for stage-3 training. Firstly, for stage-1 training, we propose the usage of linear combination of three loss functions: cross-entropy, self-supervised contrastive loss and supervised contrastive loss in a multi-tasking manner. We introduce radial and cosine margin into the formulation of self-supervised and supervised contrastive loss to pull feature embeddings of same class closer together compared to feature embeddings of different classes. Weights learned over stage-1 training are subsequently fine-tuned over cross-entropy multi-tasking loss in stage-2. These stage-2 weights are transfer learned and are further fine-tuned in stage-3 training. For stage-3 training, we propose the usage of spatial context aware multi-tasking combined loss function. This loss function leverages on the fine-grained spatial features obtained from HiResCAM. These are processed in parallel with features obtained from XGrad-CAM (sensitivity and conservation axioms satisfying features) to further supervise the learning of hierarchical yoga pose classifier. We exemplify our methodology on the publicly available Yoga-82 large-scale dataset. We report peak Top-1 YPC accuracy of 95.89% over 6 pose classes (Yoga-6), 93.85% over 20 pose classes (Yoga-20) and 90.0% over 82 pose classes (Yoga-82). Our proposed method achieves 6.1% improvement over Top-1 classification accuracy in Yoga-6 hierarchy, 9.3% improvement in Yoga-20 hierarchy and 10.9% improvement in Yoga-82 hierarchy in comparison with state-of-the-art (SOTA) methodology. We achieve the current best Top-1 classification accuracies in all the three YPC hierarchies. [Display omitted] • Novel spatial context aware multi-tasking multi-stage end-to-end training method. • Improved spatial context awareness, evident through improved CAM explanations. • Improved inter class margins and intra class compactness. • Achieved SOTA hierarchical yoga pose classification accuracies on Yoga-82 dataset. [ABSTRACT FROM AUTHOR]

Imbalanced data severely hinder the classification performance of learning-based algorithms and attract a great deal of attention from researchers. The undersampling method of selecting the majority class from samples through the prototype is one of the most common techniques to address class imbalance. However, traditional undersampling-based methods inherently lead to information loss. Meanwhile, they tend to perform undersampling for the majority class using distance-based locality methods, resulting in completely ignoring the density of the minority class samples. To this end, this paper proposes a novel undersampling method that incorporates the density distribution information of the minority class. Specifically, the probability density distribution of the minority class samples is learned by kernel density estimation, and the majority class samples located in the high-density band of the minority class are removed through filtering. Based on this, sampling fitness is proposed to evaluate the desirable value of each majority class sample to select information-rich samples. The research was carried out in 25 publicly available datasets and compared with state-of-art methods. The results show that our approach offers clear superiority. • The method learns the distribution of the minority class with time complexity O (n l o g n). • The sampling fitness combined with the roulette operator reduces information loss. • The experiments confirmed the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Data quality estimation has become an important research axis in applications when defining useful information is required. Determining useful information is an essential task in the learning process. One important aspect thereby is to find optimal and sufficient information that has a good quality and which is complimentary to improve the performance of the decision-making system. In this paper, a survey of data quality is operated in the possibilistic framework. The main objective of this work is the development of a possibilistic approach that aims to extract useful information from imperfect knowledge. The new method consists of two main steps, the classification of feature quality and the determination of the optimal feature subset. The optimal feature subset is deduced according to the feature classification technique and the feature complementarity. To show the performance of the proposed approach, the new method has been evaluated, firstly, on a synthetic dataset, and then on different benchmark databases. Later, the new method is evaluated on two real databases to gain a better insight into the performance of this method. Experiments showed the effectiveness of the proposed method in extracting useful information using the optimal feature subset. Experimental results illustrate significant accuracy rates compared with different earlier methods, especially when the system complexity increases. • This work aims to develop a possibilistic approach for extracting useful data. • Our method involves classifying features based on their quality and defining the optimal subset • Our method is evaluated on synthetic dataset, benchmark databases and two real databases. [ABSTRACT FROM AUTHOR]

In various areas of real life, Multivariate Time Series Classification (MTSC) is widely used. It has been the focus of attention of many researchers, and a number of MTSC methods have been proposed in recent years. These methods tend to focus on the features in only a single domain. However, they have ignored the correlation and complementarity between the features in a space of multiple domains. In this paper, a novel MTSC method based on fusion features (MTSC_FF) is presented to address this problem. Firstly, MTSC_FF extracts the frequency domain features using an attention layer based on continuous wavelet transform. In parallel, MTSC_FF extracts long-range dependency features from the time domain, using a sparse self-attention layer. Simultaneously, MTSC_FF obtains spatial correlations between multivariate time series dimensions via Kendall coefficient. Then, a graph neural network is used to fuse all features. Finally, the fusion features are used to predict the classification labels by means of the fully connected layer. The experimental results obtained on the UEA datasets show that the proposed method can achieve high accuracy. In addition, the proposed method can visualize the classification-dependent features, which is an improvement in the interpretability of the results. • A multivariate time series classification method is proposed. • Kendall coefficients are used to represent the spatial correlation. • Extensive experiments demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Imbalanced class distributions in machine learning, where the minority class is often under-represented, pose a substantial challenge. Synthetic Minority Over-sampling Technique (SMOTE) has been widely employed to address this issue by generating synthetic minority samples through interpolation. Despite its popularity, SMOTE exhibits certain drawbacks caused by the implementation of random interpolation samples. In this paper, we introduce a new data level technique for oversampling, called Fuzzy C-Means Center-SMOTE (FCM-CSMOTE), which generates synthetic samples in each cluster using its center considered as the memory of the main data components. We demonstrate that the proposed selective strategy has a very low probability to generate noise. The experimental results demonstrate that the proposed method performs better than the state-of-the-art approaches on 21 real unbalanced data sets (regular and large size data set) in terms of several metrics, including Geometric Mean (GM), F-Measure (FM), Area Under the Curve (AUC), and Accuracy. [ABSTRACT FROM AUTHOR]

Identifying differences between groups is one of the most important knowledge discovery problems. The procedure, also known as contrast sets mining, is applied in a wide range of areas like medicine, industry, or economics. In the paper we present RuleKit-CS, an algorithm for contrast set mining based on separate and conquer – a well established heuristic for decision rule induction. Multiple passes accompanied with an attribute penalization scheme provide contrast sets describing same examples with different attributes, distinguishing presented approach from the standard separate and conquer. The algorithm was also generalized for regression and survival data allowing identification of contrast sets whose label attribute/survival prognosis is consistent with the label/prognosis for the predefined contrast groups. This feature, not provided by the existing approaches, further extends the usability of RuleKit-CS. Experiments on over 130 data sets from various areas and detailed analysis of selected cases confirmed RuleKit-CS to be a useful tool for discovering differences between defined groups. In particular, the presented method identified contrast sets with higher support and precision than popular competitors like STUCCO, CSM-SD, or pysubgroup. The algorithm was implemented as a part of the RuleKit suite. • Combining multiple separate and conquer models renders reliable contrast sets. • Attribute penalization ensures contrast set diversity. • Regression and survival contrast sets reveal new, interesting knowledge. • Numerical attributes and missing values are supported by the algorithm. • Usability of the presented approach was confirmed on over 130 data sets. [ABSTRACT FROM AUTHOR]

Active learning is focused on minimizing the effort required to obtain labeled data by iteratively choosing fresh data samples for training a machine learning model. One of the primary challenges in active learning involves the selection of the most informative instances for labeling by an annotation oracle at each iteration. A viable approach is to develop an active learning strategy that aligns with the performance of a meta-learning model. This strategy evaluates the quality of previously selected instances and subsequently trains a machine learning model to predict the quality of instances to be labeled in the current iteration. This paper introduces a novel approach to learning for active learning, wherein instances are chosen for labeling based on their potential to induce the most substantial change in the current classifier. We explore various strategies for assessing the significance of an instance, taking into account variations in the learning gradient of the classification model. Our approach can be applied to any classifier that can be trained using gradient descent optimization. Here, we present a formulation that leverages a deep neural network model, which has not been extensively explored in existing learning-to-active-learn methodologies. Through experimental validation, our approach demonstrates promising results, especially in scenarios where there are limited initially labeled instances, particularly when the number of labeled instances per class is extremely limited. • A new meta-active learning strategy for deep neural network models. • The instance selection problem is modeled as a regression problem. • Four strategies of instance importance scoring by considering gradient variations. • Experiments have shown good results when few initially labeled objects are available. • A dimensionality reduction step for the instances improves the performances. [ABSTRACT FROM AUTHOR]

The challenge of high-dimensional feature selection (FS) lies in the search technique, which needs to consider both minimizing the size of feature subset and maximizing the classification accuracy. Recently, multi-objective evolutionary algorithms (MOEAs) have shown excellent performance in solving FS tasks. However, most existing MOEAs struggle to effectively balance these two conflicting objectives when solving high-dimensional FS tasks. To cover this issue, in this paper, a decomposition-based multi-population evolutionary algorithm is proposed, called MPEA-FS. In the initialization stage, a multi-population generation strategy (MGS) based on feature weights (MGS) was adopted, with each population corresponding to a search subspace, which can improve the algorithm's search ability. During the evolutionary stage, an external population is designed to integrate the excellent feature subsets of multiple populations to achieve knowledge sharing between them. In addition, to reduce the feature dimension, a feature reduction strategy (FRS) is proposed, which can remove unimportant features while maintaining classification accuracy. Extensive experiments are performed on 13 high-dimensional datasets, and the proposed algorithm is compared with 5 state-of-the-art FS methods proposed in the last 3 years on multiple metrics. The experimental results indicate that MPEA-FS can achieve higher classification accuracy on most datasets, and the number of selected features is also competitive. • A decomposition-based multi-population feature selection algorithm is proposed. • The generation of multi-population is based on a novel feature weight method. • Multi-population knowledge sharing strategy based on elite population is proposed. • Evolution operator based on guide vector can correct the feature subset effectively. • Obtained higher classification accuracy and competitive number of features. [ABSTRACT FROM AUTHOR]

Imbalanced data classification, where the class distribution exhibits significant skewness, presents a challenging problem in binary classification tasks. This issue is particularly pronounced for high-dimensional data, as the presence of unequal class proportions can significantly degrade the performance of classifiers. Existing approaches to address this problem involve undersampling the majority class or oversampling the minority class to create balanced samples, thereby improving classification performance. However, extending these sampling methods to high-dimensional data and mixed data, which includes categorical variables, is nontrivial due to the need for approximating attribute distributions. In this paper, we propose a novel sampling strategy that incorporates raking and relabeling procedures to construct balanced samples by imputing attribute values from the majority class to the minority class. Our proposed algorithms demonstrate comparable performance to popular existing methods, while offering greater flexibility in accommodating diverse data shapes and attribute sizes. The practical appeal of our sampling algorithm lies in its ability to generate synthetic data for oversampling without the reliance on density estimation and its capability to handle mixed-type variables seamlessly. Furthermore, our sampling strategy exhibits robustness across different classifiers, as the choice of classifier does not significantly impact classification performance. [ABSTRACT FROM AUTHOR]

Dictionary learning has gradually attracted attention due to its powerful feature representation ability. However, the time-shift property of collected signals hinders the recognition of various bearing states. In addition, existing dictionary learning methods are mostly designed based on a single domain, while common data fusion methods used in data-driven cannot be directly extended to dictionary learning. In this paper, a novel adaptive generalized domain data fusion-driven kernel sparse representation classification method (AGDFDK-SRC) is proposed. First, to avoid the effect of the time-shift property on dictionary learning, a class-specific kernel sub-dictionary learning method is proposed, by which the non-linear signal data is mapped into high-dimensional feature space via a kernel trick. Second, the class-specific kernel sub-dictionaries are learned by kernel K-singular value decomposition in a data-driven manner. Then, an adaptive generalized domain data fusion strategy is developed for dictionary learning, which implements data fusion of multiple domain signals to enhance the feature mining ability and representation ability of the learned dictionary. Finally, a kernel sparse classification method is designed to achieve intelligent bearing fault diagnosis. Two bearing datasets are exploited to verify the recognition performance of AGDFDK-SRC, indicating that the AGDFDK-SRC obtains superior average classification accuracies of 98.23% and 99.50%, respectively. [ABSTRACT FROM AUTHOR]

The verbalizer, which serves to map label words to class labels, is an essential component of prompt-tuning. In this paper, we present a novel approach to constructing verbalizers. While existing methods for verbalizer construction mainly rely on augmenting and refining sets of synonyms or related words based on class names, this paradigm suffers from a narrow perspective and lack of abstraction, resulting in limited coverage and high bias in the label-word space. To address this issue, we propose a label-word construction process that incorporates scenario-specific concepts. Specifically, we extract rich concepts from task-specific scenarios as label-word candidates and then develop a novel cascade calibration module to refine the candidates into a set of label words for each class. We evaluate the effectiveness of our proposed approach through extensive experiments on five widely used datasets for zero-shot text classification. The results demonstrate that our method outperforms existing methods and achieves state-of-the-art results. • Retrieving scenario-specific concepts as label word candidates. • Proposing a novel class-name-free cascade approach for label-word refining. • Constructing a verbalizer that has wide-ranging coverage and minimal bias. • Reporting fresh state-of-the-art results. [ABSTRACT FROM AUTHOR]

Over the past few decades, short text classification has emerged as a critical downstream task in natural language processing (NLP). One crucial classification research issue is how to advance semantic understanding considering the short length, feature sparsity, and high ambiguity in short texts. Recently, prompt-tuning has been proposed to insert a template into the input and convert text classification tasks into equivalent cloze-style tasks. However, among most of the previous approaches, either the crafted template methods are time-consuming and labor-intensive, or automatic prompt generation methods cannot achieve satisfied performance. In this paper, we introduce a novel approach called Soft Knowledgeable Prompt-tuning for short text classification. Our method considers both the template generation and classification performance to construct prompts for label prediction. We employ five different strategies to expand the label words space for modifying soft prompts, and the integration of these strategies is used as the final verbalizer. Despite being automatic, experimental results show that our method achieved more desirable performance even than the crafted template methods, outperforming the state-of-the-art by more than 14 Accuracy points on four well-known benchmarks. • Both the automatic prompt generation and text classification performance are achieved. • 5 different strategies are used to construct verbalizer effectively and efficiently. • The comprehensive experiments evaluate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

The long-tailed distribution data poses many challenges for machine learning because the tail classes are extremely scarce. Long-tailed data augmentation is a powerful technique for enriching the tail class diversity. However, existing methods often treat each class independently, assuming that classes are isolated from each other. These approaches overlook the presence of easily confused tail classes, making it challenging for models to distinguish between them accurately. In this paper, we propose a long-tailed classification method based on data augmentation, which utilizes multi-granularity knowledge to select and combine easily confused tail samples, thereby enhancing the classification performance of these samples. First, we utilize multi-granularity knowledge and semantic relation trees to build a class relation matrix. This matrix records the relationship between classes and helps the model search for easily confused classes from bilateral branch samplers. Second, we crop and combine the easily confused head and tail class samples in a foreground–background manner to generate new samples, thereby augmenting the model training. The extensive head class knowledge is transferred to the scarce tail class samples through the combination of fore-background, and the discriminative and generalized abilities of the model are improved. The experimental results affirm the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Text classification is the process of automatically categorizing text documents into predefined labels, gaining increasing importance with the growing volume of data. In the vector space model, terms within documents must be quantified to be used with classifiers. The abundance of unique terms associated with documents can lead to unwieldy vectors. To address this issue, feature selection, reducing the number of terms by removing irrelevant terms, is a common solution. In this paper, a novel feature selection method called Amount of ReDistribution to Establish Neutrality, ARDEN, is proposed for the task of text classification. ARDEN, is designed with statistical distance perspective by measuring the distance of a term to its neutral counterpart, which represents the least distinguishing term having uniform document frequencies across all classes. ARDEN introduces a method to measure the related distance, providing insights into the degree of deviation of the term from neutrality, thus capturing its distinguishing power. ARDEN is experimentally tested against state-of-the-art feature selection methods. The results suggest that it is a competitive feature selection method, exhibiting superior performance in terms of summary statistics, μ and δ ′. Furthermore, ARDEN is designed for multi-class text classification problems, which does not require a globalization function. Additionally, the proposed method is compared against Wasserstein statistical distance metric, and clearly outperforms it. Finally, we also propose δ ′ evaluation criterion in order to facilitate the interpretation of experimental outcomes across multiple feature sizes. • A novel feature selection, ARDEN, is proposed for text classification. • ARDEN measures the amount of redistribution to establish neutrality for ranking. • ARDEN is designed for multi-class problems and does not need globalization functions. • Experimental results suggest that ARDEN is a competitive feature selection method. • δ ′ is proposed to interpret the results of multiple features. [ABSTRACT FROM AUTHOR]

Skin diseases are reported to contribute 1.79% of the global burden of disease. The accurate diagnosis of specific skin diseases is known to be a challenging task due, in part, to variations in skin tone, texture, body hair, etc. Classification of skin lesions using machine learning is a demanding task, due to the varying shapes, sizes, colors, and vague boundaries of some lesions. The use of deep learning for the classification of skin lesion images has been shown to help diagnose the disease at its early stages. Recent studies have demonstrated that these models perform well in skin detection tasks, with high accuracy and efficiency. Our paper proposes an end-to-end framework for skin lesion classification, and our contributions are two-fold. Firstly, two fundamentally different algorithms are proposed for segmenting and extracting features from images during image preprocessing. Secondly, we present a deep convolutional neural network model, S-MobileNet that aims to classify 7 different types of skin lesions. We used the HAM10000 dataset, which consists of 10000 dermatoscopic images from different populations and is publicly available through the International Skin Imaging Collaboration (ISIC) Archive. The image data was preprocessed to make it suitable for modeling. Exploratory data analysis (EDA) was performed to understand various attributes and their relationships within the dataset. A modified version of a Gaussian filtering algorithm and SFTA was applied for image segmentation and feature extraction. The processed dataset was then fed into the S-MobileNet model. This model was designed to be lightweight and was analyzed in three dimensions: using the Relu Activation function, the Mish activation function, and applying compression at intermediary layers. In addition, an alternative approach for compressing layers in the S-MobileNet architecture was applied to ensure a lightweight model that does not compromise on performance. The model was trained using several experiments and assessed using various performance measures, including, loss, accuracy, precision, and the F1-score. Our results demonstrate an improvement in model performance when applying a preprocessing technique. The Mish activation function was shown to outperform Relu. Further, the classification accuracy of the compressed S-MobileNet was shown to outperform S-MobileNet. To conclude, our findings have shown that our proposed deep learning-based S-MobileNet model is the optimal approach for classifying skin lesion images in the HAM10000 dataset. In the future, our approach could be adapted and applied to other datasets, and validated to develop a skin lesion framework that can be utilized in real-time. [ABSTRACT FROM AUTHOR]

Although existing text classification methods have achieved SOTA results on most tasks, their generalization ability is inferior to their excellent performance on a single task. For this issue, existing research mainly improves the generalization of text classification models by adding label semantics to model training and allowing models to see label semantic information. However, its generalization ability is still limited by the description and quantity of labels. This paper presents a Contrastive Classification method based on contrastive learning to enhance the generalization of text classification models. By transforming all text classification tasks into semantic understanding tasks, the method strengthens the correlation between pre-training tasks and downstream tasks, better preserving the generalization of pre-trained models. At the same time, it unifies the training objectives of all text classification tasks, reducing the differences between different tasks. We verified the model's generalization ability on 17 different zero-shot tasks, verified the model's cross-domain generalization ability and growth potential on 3 question–answer and 2 reading comprehension tasks, and verified the model's performance on 12 supervised text categorization tasks. Experimental results show that Contrastive Classification frees text classification models from the limitations of classification labels and improves them in different dimensions: (1) Robustness, (2) Cross-domain generalization ability, and (3) Growth potential. We plan to release our code at https://github.com/liangyi-qianwan/Contrastive-classification-A-label-independent-generalization-model-for-text-classification/tree/main. [ABSTRACT FROM AUTHOR]

Generating pseudo unseen samples is currently an effective approach for addressing the generalized zero-shot classification (GZSC) problem. However, in practical scenarios, the test set may contain open-set samples without semantic representations. Generalized zero-shot open-set classification (GZSOSC) extends GZSC tasks by simultaneously dealing with samples from seen, unseen, and open-set classes. In this paper, we propose a novel method to learn selective-generative feature representations (SGFR) to tackle the GZSOSC problem. Firstly, to handle the lack of unseen samples, we introduce a simple yet effective unseen feature generation method that leverages the seen–unseen relationships. Through an efficient alternating optimization strategy, we learn the seen–unseen relationships and the unseen visual centers. Secondly, to address the lack of open-set samples, we focus on learning a tightly clustered space for both seen and unseen classes. This enables effective open-set feature selection. We utilize the selected open-set samples to generate high-quality open-set features, thus enhancing the diversity of open-set samples. Extensive experiments are conducted to demonstrate the effectiveness of SGFR in handling the GZSOSC task. [ABSTRACT FROM AUTHOR]

• 140 papers that deal with the blocking flow shop scheduling problem are reviewed. • Papers are classified according to the purpose and objective function adopted. • Tables allow the reader to find the interested literature. • Based on the gaps discovered, suggestions for future research are pointed out. This paper presents a literature review on the m -machine flow shop scheduling problem with blocking conditions. In total, 139 papers are reviewed and classified, ranging from 1969 up to early 2019. Results showed that makespan was the objective function most adopted by the researchers, with 62% of the total covered, followed by multi-objective based (12%), total flow time-based (11%), due date-based (7%), stochastic based functions (6%) and cycle time (3%). Regarding the purpose of the paper, approximately 92% of the papers proposed solution methods, where 76% of the papers developed heuristic methods and 16% of exact methods, and 8% of the papers considered the analysis of the problem and literature reviews. Directions for future researches include the proposition of solution methods for mono-objective functions as total flow time-based and due date-based, development of solution methods for the m -machine flow shop with RCb and RCb* constraints and adoption of more than one additional constraint to the problem. [ABSTRACT FROM AUTHOR]

• This paper proposes the Recursive General Regression Neural Network Oracle. • The algorithm was compared to several other classifiers. • It had the highest performance metrics such as accuracy and area under the curve. • The algorithm had also bested the literature in the performance metrics. This research introduces the Recursive General Regression Neural Network Oracle (R-GRNN Oracle) and is demonstrated on several binary classification datasets. The traditional GRNN Oracle classifier (Masters et al., 1998) combines the predictive powers of several machine learning classifiers by weighing the amount of error each classifier has on the final predictions. Each classifier is assigned a weight based on the percentage of errors it contributes to the final predictions as the classifiers evaluate the dataset. The proposed R-GRNN Oracle is an enhancement to the GRNN Oracle in which the proposed algorithm consists of an oracle within an oracle – where the inner oracle acts as a classifier with its own predictions and error contribution. By combining the inner oracle with other classifiers, the R-GRNN Oracle produces superior results. The classifiers considered in this study are: Support Vector Machine (SVM), Multilayer Perceptron (MLP), Probabilistic Neural Network (PNN), Gaussian Naïve Bayes (GNB), K-Nearest Neighbor (KNN), and Random Forest (RF). To demonstrate the effectiveness of the proposed approach, several datasets were used, with the primary one being the publicly available Spambase dataset. The predictions of SVM, MLP, KNN, and RF were used to create the first GRNN Oracle, which was then enhanced with the high performances of SVM and RF to create the second oracle, the R-GRNN Oracle. The combined recursive model was 93.24% accurate using 10-fold cross validation, higher than the 91.94% of the inner GRNN Oracle and the 91.29% achieved by RF, the highest performance by a stand-alone classifier. The R-GRNN Oracle was not only the most accurate, but it also had the highest AUC, sensitivity, specificity, precision, and F1-score (97.99%, 91.86%, 94.40%, 93.28%, and 92.57%, respectively). The research contribution of this paper is introducing the concept of recursion (a concept not fully explored in machine learning models and applications) and testing this structure's ability on further enhancing the performance of the traditional oracle. The recursive model has also been applied to several other datasets: The Human Resources, Bank Marketing, and Monoclonal Gammopathy of Undetermined Significance (MGUS) datasets. The results of these implementations are summarized in this paper. [ABSTRACT FROM AUTHOR]

• Empirical demonstration of Sparse classification methods on text data. • Minimum residual error and class wise dictionaries based methods are most efficient. • Proposed a dynamic dictionary refinement algorithm. • Proposed work improves computational speed and classification performance. • Dictionary refinement procedure addressed the problem of over-reliance on training data. Classification of text based on its substance is an essential part of analysis to organize enormously large text data and to mine the salient information contained in it. It is gaining greater attention with the surge in the volume of on-line data available. Classical algorithms like k -NN (k-nearest neighbor), SVM (Support Vector Machine) and their variations have been observed to yield only reasonable results in addressing the problem, leaving enough room for further improvement. A class of algorithms commonly referred to as Sparse Methods has been emerged recently from compressive sensing and found numerous effective applications in many areas of data analysis and image processing. Sparse Methods as a tool for text analysis is an alley that is largely unexplored rigorously. This paper presents exploration of sparse representation-based methods for text classification. Based on the success of sparse representation based methods in different areas of data analysis, we intuitively hypothesized that it should work well on text classification problems as well. This paper empirically reinforces the hypothesis by testing the method on Reuters and WebKB data sets. The empirical results on Reuters and WebKB benchmark data show that it can outperform classical classification algorithms like SVM and k -NN. It has been observed that obtaining the basis of representation and sparse codes are computationally costly operations affecting the performance of the system. We also propose a class-wise dictionary refinement algorithm and dynamic dictionary selection algorithm to make sparse coding faster. The addition of dictionary refinement to the classification system not only reduces the time taken for sparse coding but also gives improved classification accuracy. The outcomes of the study are empirical verification of sparse representation classifier as a text classification tool and a computationally efficient solution for the bottleneck operation of sparse coding. [ABSTRACT FROM AUTHOR]

• The prediction task of CoIL challenge 2000 is addressed in the paper. • Three different methods are proposed for direct mailing problem of CoIL challenge 2000. • The proposed methods outperform the method proposed by the winner of the challenge. • The proposed methods overcome, also, the unbalanced dataset issue of the problem. This paper presents some approaches based on data mining techniques to solve the prediction task of Computational Intelligence and Learning (CoIL) Challenge 2000. The prediction task of the contest is a direct mailing problem and the goal is to improve its response rate. The main issue in this competition is the incompatibility of the dataset in which the distribution of the classes of the target attribute is highly unbalanced. This in turn causes high error rate in identifying the minority class samples. Three different level methods including data-level, algorithm-level, and hybrid method are used to overcome this issue. The specificity, sensitivity, precision-recall, and ROC criteria are employed to compare the performance of the methods. Among the methods proposed in this paper, the best one performs much better than the winner of the competition. [ABSTRACT FROM AUTHOR]

Identifying standard fetal ultrasound (US) planes with key anatomical structures during mid-pregnancy prenatal screening is crucial for measuring fetal growth parameters and early detection of abnormalities. However, obtaining these standard planes is laborious and time-consuming and depends on the clinical experience of sonographers. Automatic detection of these planes can aid sonographers in identifying the correct standard planes. In recent times, various deep learning techniques have developed to automate the detection of standard fetal US planes. However, a common limitation among these approaches is their dependence on a single model prediction to make the final decision, which introduces the possibility of inaccuracies. Therefore, we propose an automated identification of commonly used standard fetal US planes based on the stacking ensemble of deep convolutional neural networks (CNN). The stacking ensemble method employs three pre-trained deep CNNs: AlexNet, VGG-19, and DarkNet-19. Softmax and random forest classifiers are used to get predictions from deep CNNs. The final prediction is made using the absolute majority voting technique. A publicly available fetal US dataset is employed to evaluate the performance of the stacking ensemble approach. The proposed ensemble model classifies fetal US planes into six distinct classes: abdomen, brain, femur, thorax, maternal cervix, and other (less commonly employed planes, such as kidney, and limbs) fetal planes. Experimental findings demonstrate that the stacking ensemble approach achieved high performance with an accuracy of 95.69%, precision of 94.02%, recall of 96.28%, F1-score of 95.08%, specificity of 99.12%, and Matthews correlation coefficient of 94.19% compared to individual deep CNN models and other competing methods. • This paper presents a stacking ensemble of CNNs to classify fetal US images. • The stacking ensemble learning model includes AlexNet, VGG-19, modified VGG-19 and DarkNet-19. • The final classification is made by using the absolute majority voting method. • A publicly available fetal US dataset is used to validate the proposed method. • Proposed method outperformed the state-of-the-art and competing algorithms. [ABSTRACT FROM AUTHOR]

In texture classification, local binary pattern (LBP) is currently one of the most widely-concerned feature encoding models. Most existing LBP-based texture classification methods are usually limited to single-kind texture features. In fact, an across-domain fusion of LBP features with other features, such as image contours, could be another potential path to promote texture classification. To enhance the feature modeling ability of LBP-based methods, this paper firstly designs a Cellular Neural Network (CellNN) with recurrent convolutions, initially trained by a simplified simulated-annealing algorithm, to extract informative image contours. For better reliability, a new three-channel contour extractor of deep CellNNs (i. e. , dCellNNs) is proposed. This extractor contains the initially-trained CellNNs of more than three layers, and it is further optimized by fine-tuning parameters. Moreover, a new weighted-base algorithm is designed to fulfill the fusion of the multi-scale texture features by LBPs and the contour features by dCellNNs to enhance feature representation. Finally, these enhanced features are concatenated together to generate the final multi-scale features of given texture image. On texture datasets KTH, Brodatz, OTC12 and UIUC, experiment results verify that the across-domain fusion of multi-scale LBPs and dCellNNs is efficient in capturing & enhancing texture features. With moderate feature dimensionality and computational costs, it could improve texture classification, acquiring an obvious accuracy increase on previous state-of-the-art ones, e.g. , a rise of 2.58% on KTH-TIPS2b, a rise of 3.11% on Brodatz, a rise of 0.71% on OTC12 and a rise of 0.42% on UIUC. • A multi-scale across-domain feature fusion of textures and contours is proposed. • The first deep model of CellNN (dCellNNs) with two-stage training is proposed. • The three-channel texture contour extractor of dCellNNs is designed. • A weighted-base fusing is designed for the across-domain fusion of features. [ABSTRACT FROM AUTHOR]

Due to the enormous amount of user-generated content being generated on the web, labeling such data is a time-consuming and expensive endeavor. As a result, we have limited annotated data and the vast majority of data are unlabeled. Analysis reveals that extracting (external) knowledge from unlabeled data and integrating it with knowledge extracted from labeled data is a beneficial task for text information processing, in particular text classification. In this paper, we present a hybrid approach for classifying sentiments that employs external knowledge, which is categorized as either general-purpose sentiment knowledge or domain-related knowledge. General-purpose sentiment knowledge is extracted from sentiment lexicons, whereas domain-related knowledge is extracted from unlabeled data from the same or related domains. Similar domains for a given domain are identified based on their similarity score in terms of overlapping features. The proposed approach utilizes both forms of external knowledge and combines them with logistic regression to train an improved classification model. The classification model uses the conventional gradient descent algorithm for optimization, and its convergence analysis indicates that it is convex and converges to the global optimum. The proposed approach is empirically evaluated and compared to three baselines and one state-of-the-art method using standard performance evaluation metrics on a multi-domain sentiment dataset. The experiment results are encouraging, demonstrating that the proposed approach considerably outperforms the baseline approaches and outperforms the state-of-the-art approach by up to 2% in terms of both f-score and accuracy. • A hybrid approach for document-level sentiment classification that makes use of external knowledge. • A domain-specific knowledge extraction approach from unlabeled text documents. • A detailed convergence analysis of the proposed optimization model used in the proposed approach. [ABSTRACT FROM AUTHOR]

Recently meta-learning-based few-shot learning methods have been widely used for relation classification. Previous work reveals that meta-learning performs poorly in scenarios where the edge probability distribution of the target domain dataset appears to be significantly different from the source domain. In this paper, we enhance the meta-learning framework with high-dimensional semantic feature extraction and hyperplane projection metrics for meta-tasks. First, we enhance the focus of BERT on entity words by adding entity markers and vector pooling. After that, the high-dimensional semantic features of the support set are extracted and transformed into hyperplanes. Finally, we obtain the classification results by calculating the projection distance between the query sample and the hyperplane. In addition, we design a auxiliary function with a plane correction factor, which can better amplify the plane spacing and reduce the degree of category confusion, which is important for solving the problem of metric spatial loss. Experiments on two real-world few-shot datasets show that our model HPN is more effective in classifying few-shot relations in the same domain and domain-adapted scenarios. And HPN is more stable on NOTA tasks. • A hyperplane projection network is proposed for few shot relation classification. • Propose an effective entity encoder that extracts better features. • First apply a novel hyperplane module to generate class-level feature. [ABSTRACT FROM AUTHOR]

Neighborhood rough set theories are commonly used in global feature selection to achieve high performance in continuous data classification. However, selecting a single feature subset to represent the entire dataset may degrade the performance when there are intra-class dissimilarities among objects. Therefore, this paper proposes a novel feature-selection method, Granule-specific Feature Selection (GFS) to select local feature subsets for continuous data classification. The feature selection approach constitutes a novel feature selection algorithm and a novel feature evaluation function and uses existing approaches for granule identification and classification with some adjustments. The neighborhood rough set theories are used in granule (subclass) identification within each class when there are no subclass label information available in the training data, while an improved k-Nearest Neighbors approach is used in classification with granule-specific feature subsets. Experimental results show GFS outperforms most of the global, class-specific, and local feature selection baselines in terms of classification performance. • Granule-specific feature selection using neighborhood rough set theories. • Feature selection while managing intra-class dissimilarities of objects. • Handle data uncertainty of continuous data when selecting features. • Improve classification performance using granule-specific feature subsets. [ABSTRACT FROM AUTHOR]

• Propose an improved random forest based on the improvement of decision trees. • Improve the evaluation mechanism for the classification effect of decision trees. • Propose a method for quantifying the diversity between decision trees. • Multiple tests verify the superiority of the proposed improved random forest. Random forest is one of the most widely used machine learning algorithms. Decision trees used to construct the random forest may have low classification accuracies or high correlations, which affects the comprehensive performance of the random forest. Aiming at these problems, the authors proposed an improved random forest based on the classification accuracy and correlation measurement of decision trees in this paper. Its main idea includes two parts, one is retaining the classification and regression trees (CARTs) with better classification effects, the other is reducing the correlations between the CARTs. Specifically, in the classification effect evaluation part, each CART was applied to make predictions on three reserved data sets, then the average classification accuracies were achieved, respectively. Thus, all the CARTs were sorted in descending order according to their achieved average classification accuracies. In the correlation measurement part, the improved dot product method was proposed to calculate the cosine similarity, i.e., the correlation, between CARTs in the feature space. By using the achieved average classification accuracy as reference, the grid search method was used to find the inner product threshold. On this basis, the CARTs with low average classification accuracy among CART pairs whose inner product values are higher than the inner product threshold were marked as deletable. The achieved average classification accuracies and correlations of CARTs were comprehensively considered, those with high correlation and weak classification effect were deleted, and those with better quality were retained to construct the random forest. Multiple experiments show that, the proposed improved random forest achieved higher average classification accuracy than the five random forests used for comparison, and the lead was stable. The G-means and out-of-bag data (OBD) score obtained by the proposed improved random forest were also higher than the five random forests, and the lead was more obvious. In addition, the test results of three non-parametric tests show that, there were significant diversities between the proposed improved random forest and the other five random forests. This effectively proves the superiority and practicability of the proposed improved random forest. [ABSTRACT FROM AUTHOR]

• A reconstruction framework is developed to classify mass news titles. • This framework can identify out-of-domain titles with in-domain words. • A cosine-based classification layer is designed to use domain knowledge. • This framework is trained on in-domain titles without out-of-domain titles. • This framework is an explainable classification approach. News title classification is a widely-used task for people to organize mass news. But traditional classification methods usually depend on a pre-defined label set, while many news articles may belong to unseen labels instead of pre-defined labels. To address this challenge, this paper develops a domain-knowledge based reconstruction framework to classify both out-of-domain (titles with unseen labels) and in-domain (titles with pre-defined labels) titles. Specifically, out-of-domain titles are identified through the reconstructed difference from autoencoder, while in-domain titles are classified to the pre-defined label with the highest cosine similarity between title and each category explanation text. Those category explanation texts are regarded as domain knowledge for in-domain aspect classification, aiming to make reconstruction framework more stable and meanwhile provide extra information for short titles. Furthermore, our method adopts BERT model to generate pre-trained embeddings for input texts. We conducted experiments on BRI (Belt and Road Initiative) news dataset and demonstrated that our method has achieved F1-score of 0.735 and 0.838 for out-of-domain title identification and in-domain aspect classification respectively. Moreover, we used titles from four countries to indicate our method's application value in cross-border business analysis. [ABSTRACT FROM AUTHOR]

The accumulation of large amounts of historical data has led to the wide application of ensemble learning over the past few decades, but the balance between the individual accuracy of base classifiers (BCs) and the diversity among these BCs is rarely considered in the construction of ensemble models. Since such a balance is crucial to the success of ensemble models, this paper proposes a Genetic Algorithm-based sequential instance selection framework to address this research gap. The novelties of the proposed framework include: transforming the balance between the individual accuracy of BCs and the diversity among BCs into a general combinatorial optimization model and designing a Genetic Algorithm-based evolutionary instance selection method to solve this model. The proposed framework not only overcomes the inherent limitations of the Genetic Algorithm in some high-dimensional tasks but also provides an explicit and automatic way to balance the accuracy and diversity by searching appropriate training data subsets for different component BCs. Based on obtained training data subsets, the component BCs of the ensemble model are generated sequentially, and their predictions are further combined with the weighted majority voting rule. Using 30 real datasets collected from various practical applications, such as medicine, business, and industry, the effectiveness of the proposed framework in constructing powerful ensemble models is examined and compared with six benchmark ensemble learning methods. In addition, the capability of the proposed framework to improve convergence performances is also examined by the comparison with the traditional Genetic Algorithm. [ABSTRACT FROM AUTHOR]

Deciding the signal length is an important challenge for one-class time-series classification (OCTSC). This paper aims to develop an OCTSC algorithm that does not require model retraining for different signal lengths. For this purpose, a distance-based one-class time-series classification approach using local cluster balance (OCLCB) is proposed. OCLCB extracts feature vectors, namely, local cluster balance (LCB), from the clustering results of sliding windows. K-means clustering is applied to the sliding windows extracted from the training signal. Then, the local prototype (LP) is calculated as the average of the local cluster balance (LCB) in the training data. Unseen scores are computed as the distance metrics between LP and LCBs in the testing data. Since the sliding window size is independent of the entire signal size, OCLCB does not need to retrain the model. This aspect gives the benefit of reducing the parameter tuning costs. The source code is uploaded at https://github.com/ToshiHayashi/OCLCB. [ABSTRACT FROM AUTHOR]

Abstract: In this paper it is introduced a recognition method for Mexican banknotes by using artificial vision. It is shown that the Mexican banknotes can be classified by extracting their color and texture features, with the RGB space and the Local Binary Patterns, respectively. We show the classification results performed with the current Mexican banknotes. We state that the proposed method can be applied to recognize banknotes of other countries which employ colors to distinguish the denominations. [Copyright &y& Elsevier]

In real-world scenarios, new audio classes with insufficient samples usually emerge continually, which motivates the study of few-shot class-incremental audio classification (FCAC) in this paper. FCAC aims to enable the model to recognize new audio classes while remembering the base ones continually. To solve the FCAC problem, the discriminability of the prototypes is vital to the model's classification performance. Thus, we proposed a method to learn the discriminative prototypes from two aspects. First, since the generalization ability of the embedding module (EM) significantly affects the discriminability of the prototypes, the proposed method employs a scheme of pseudo-episodic incremental training to train the EM by simulating the test scenario. Second, to enable the model to achieve a balanced classification performance on both base and new audio classes, the proposed method employs a selective-attention module to adjust different prototypes to enhance their discriminability. Extensive experimental results demonstrate that the proposed method achieves state-of-the-art performance in solving the FCAC problem. Notably, the proposed method achieves a comprehensive performance score (CPS) of 87.82% and 59.25% on the Neural Synthesis musical notes of 100 classes (NSynth-100) and Free sound clips of 89 classes (FSC-89) datasets, respectively, which outperforms the comparison methods. Our code is available at https://github.com/chester-w-xie/DPL_FCAC. • The few-shot class-incremental audio classification problem is studied in this paper. • The proposed method expands the model with discriminative prototypes. • A scheme is proposed to enhance the generalization ability of the embedding module. • A selective-attention-based module is proposed for prototype adjustment. • The proposed method achieves state-of-the-art performance in solving the problem. [ABSTRACT FROM AUTHOR]

This paper proposes a distance-based index to evaluate the input data and the data flowing through layers of a convolutional neural network in classification problems. This shape-less index is proposed based on the class separability and data complexity concepts which are explained in a classification problem. A dataset is complex if many of its samples are located in overlapping regions belonging to different classes, making the classification challenging. In addition, a class is separable if its samples are not located in the overlapped area. The proposed index counts the number of samples in the overlapped area normalized by the number of all samples, measuring the complexity of the dataset. The paper explains the idea of dividing features into exclusive and common features, and identifies the conditions for separability based on this concept. After that, probability evidence shows why the Separation index can be included in complexity measurement indices. The benefits of using the introduced index are shown through many case studies. In the first case study, the proposed index ranks some known supervised datasets from less to more challenging. In the second case study, the separability of Dataflow is evaluated through layers of two pre-trained VGG-16 networks in the classification of CIFAR-10, CIFAR-100, and Caltech-101. The evaluation of Dataflow is also shown through a trained ResNet-18 model for the Fashion-MNIST dataset in the subsequent case study. Finally, it is shown that the correct classification rate and the separation index are almost equivalent through layers, particularly when the separation index is increased. [ABSTRACT FROM AUTHOR]

Highlights • A novel modeling strategy for Decision Trees is proposed. • Interactive basis functions induce oblique and non-linear decision boundaries. • Benchmarking shows potential gains in accuracy and/or parsimony of models. • Empirical examples illustrate the application of the modeling strategy. • Decision charts are introduced to improve the interpretability of models. Abstract Decision Trees (DTs) are a machine learning technique widely used for regression and classification purposes. Conventionally, the decision boundaries of Decision Trees are orthogonal to the features under consideration. A well-known limitation of this is that the algorithm may fail to find optimal partitions, or in some cases any partitions at all, depending on the underlying distribution of the data. To remedy this limitation, several modifications have been proposed that allow for oblique decision boundaries. The objective of this paper is to propose a new strategy for generating flexible decision boundaries by means of interactive basis functions (IBFs). We show how oblique decision boundaries can be obtained as a particular case of IBFs, and in addition how non-linear decision boundaries can be induced. One attractive aspect of the strategy proposed in this paper is that training Decision Trees with IBFs does not require custom software, since the functions can be precalculated for use in any existing implementation of the algorithm. Since the underlying mechanisms remain unchanged there is no substantial computational overhead compared to conventional trees. Furthermore, this also means that IBFs can be used in any extensions of the Decision Tree algorithm, such as evolutionary trees, boosting, and bagging. We conduct a benchmarking exercise to understand under which conditions the use of IBFs can improve model the performance. In addition, we present three empirical applications that illustrate the approach in classification and regression. As part of discussing the empirical applications, we introduce a device called decision charts to facilitate the interpretation of DTs with IBFs. Finally, we conclude the paper by outlining some directions for future research. [ABSTRACT FROM AUTHOR]

Highlights • Novel Neutrosophic Rule-based Classification System (NRCS) models are proposed. • Our dataset has 553 drugs that were bio-transformed into liver. • Three rough set-based algorithms are used for features selection. • Three sampling algorithms are used to obtain balanced data. • The proposed model obtained promising results. Abstract Measuring toxicity is an important step in drug development. However, the current experimental methods which are used to estimate the drug toxicity are expensive and need high computational efforts. Therefore, these methods are not suitable for large-scale evaluation of drug toxicity. As a consequence, there is a high demand to implement computational models that can predict drug toxicity risks. In this paper, we used a dataset that consists of 553 drugs that biotransformed in the liver. In this data, there are four toxic effects, namely, mutagenic, tumorigenic, irritant and reproductive effects. Each drug is represented by 31 chemical descriptors. This paper proposes two models for predicting drug toxicity risks. The proposed models consist of three phases. In the first phase, the most discriminative features are selected using rough set-based methods to reduce the classification time and improve the classification performance. In the second phase, three different sampling algorithms, namely, Random Under-Sampling, Random Over-Sampling, and Synthetic Minority Oversampling Technique (SMOTE) are used to obtain balanced data. In the third phase, the first proposed model employs the Neutrosophic Rule-based Classification System (NRCS), and the second model uses Genetic NRCS (GNRCS) to classify an unknown drug into toxic or non-toxic. The experimental results proved that the proposed models obtained high sensitivity (89–93%), specificity (91–97%), and GM (90–94%) for all toxic effects. Overall, the results of the proposed models indicate that it could be utilized for the prediction of drug toxicity in the early stages of drug development. [ABSTRACT FROM AUTHOR]

Highlights • Mammographic masses classification is dominated by ANN, SVM and KNN. • Grammars and syntactic approaches have not been used so far. • Genetic algorithm was the most used technique to reduce feature dimensionality. • Most of the published papers are database-dependent. • Results are not comparable due to the lack of a common benchmark. Abstract Breast cancer is one of the most common and deadliest cancers that affect mainly women worldwide, and mammography examination is one of the main tools to help early detection. Several papers have been published in the last decades reporting on techniques to automatically recognize breast cancer by analyzing mammograms. These techniques were used to create computer systems to help physicians and radiologists obtain a more precise diagnosis. The objective of this paper is to present an overview regarding the use of machine learning and pattern recognition techniques to discriminate masses in digitized mammograms. The main differences we found in the literature between the present paper and the other reviews are: 1) we used a systematic review method to create this survey; 2) we focused on mass classification problems; 3) the broad scope and spectrum used to investigate this theme, as 129 papers were analyzed to find out whether mass classification in mammograms is a problem solved. In order to achieve this objective, we performed a systematic review process to analyze papers found in the most important digital libraries in the area. We noticed that the three most common techniques used to classify mammographic masses are artificial neural network, support vector machine and k-nearest neighbors. Furthermore, we noticed that mass shape and texture are the most used features in classification, although some papers presented the usage of features provided by specialists, such as BI-RADS descriptors. Moreover, several feature selection techniques were used to reduce the complexity of the classifiers or to increase their accuracies. Additionally, the survey conducted points out some still unexplored research opportunities in this area, for example, we identified that some techniques such as random forest and logistic regression are little explored, while others, such as grammars or syntactic approaches, are not being used to perform this task. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing is a recent model in which a group of mobile users uses their smart devices such as smartphones or wearable devices to cooperatively perform a large-scale sensing task. In this paper, a novel model will be introduced for recognizing/classifying human activities that were collected from sensor units on the chest, legs, and arms. The proposed model employed the k -Nearest Neighbor ( k -NN) classifier which is one of the most common classifiers. k -NN has only one parameter, k , to determine the number of selected nearest neighbors to the test or unknown samples for predicting the class labels of the unknown samples. Searching for the value of k which has a great impact on the classification performance is difficult especially with high dimensional data. This paper employs the Particle Swarm Optimization (PSO) algorithm to search for the optimal value of the k parameter in the k -NN Classifier. This paper shows first experimentally how the PSO in the proposed algorithm searches for the optimal value of k parameter to reduce the misclassification rate of the k -NN classifier. Then, in the second experiment, ten standard datasets are utilized to benchmark the performance of the proposed algorithm. For verification, the results of the PSO- k NN algorithm are compared with two well-known algorithms: Genetic Algorithm (GA) and Ant Bee Colony Optimization (ABCO). In the third experiment, the proposed PSO- k NN algorithm was employed for recognizing human activities. The experimental results proved that the PSO- k NN algorithm is able to find the optimal or near optimal value(s) of the k parameter which enhances the accuracy of k -NN classifier. The results also demonstrated lower error rates compared when GA and ABCO algorithms. [ABSTRACT FROM AUTHOR]

Online social networks (OSNs) are receiving great attention from the research community for different purposes, such as event detection, crisis management, and forecasting, among others. The increasing amount of research conducted with social networks opens the need for a classification methodology regarding trends in the field. This work does not cover all types of social networks; it focuses on the analysis of microblogs as a data source in the context of recommender systems (RSs). The main goal of this work is to provide authors with insights on the trends of academic literature reviews in the proposed context and to provide a comparison of different research approaches. The authors searched for up-to-date research papers related to RS methods using microblogs within a time period of five years, from 2012 to January 2018. Starting from 2012, a significant amount of research related to the subject field of RSs was conducted and identified by the authors of this work. After the filtering process, 39 papers were finally selected from journals and conferences in four different databases related to Internet technologies (i.e., IEEE, ACM, Science Direct, and Springer). A general classification presented in this work is then adopted and used to describe state-of-the-art social network recommendation approaches for microblogging. This work can be extended in the future to include novel methodologies and trends of RSs for microblogs. [ABSTRACT FROM AUTHOR]