Metric learning aims to learn a distance to measure the difference between two samples, and it plays an important role in pattern recognition tasks. Most of the existing metric learning methods rely on pairs of samples. However, the importance of sample pairs varies greatly because of possible noise and the difference between samples and the decision boundaries. In this paper, we propose a robust hierarchical

In multi-label learning, exploiting label correlation, alleviating class imbalance and learning label-specific features have been hot topics to increase classification performance. In the paper, we propose a method to address the three issues simultaneously. The method, named LPLC-LA, builds a Bayesian model by exploiting the local positive and negative label correlations with label awareness. LPLC-LA

For the first time, the ensemble feature selection is modeled as a Multi-Criteria Decision-Making (MCDM) process in this paper. For this purpose, we used the VIKOR method as a famous MCDM algorithm to rank the features based on the evaluation of several feature selection methods as different decision-making criteria. Our proposed method, EFS-MCDM, first obtains a decision matrix using the ranks of

The nonnegative matrix factorization (NMF) has been widely used because it can accomplish both feature representation learning and dimension reduction. However, there are two critical and challenging issues affecting the performance of NMF models. One is the selection of matrix factorization rank, while most of the existing methods are based on experiments or experience. For tackling this issue, an

Session-based recommendation (SR) is important in online applications for its ability to predict user’s next interactions solely based on ongoing sessions. To recommend proper items at proper time are two key aspects in SR. The sequence of items in a session implies user’s preferences shift, which may give us clues about when the user interacted. The graph constructed based on a session can give latent

Three different examinations for any course are primarily defined in higher education in Turkey: midterm, final and make-up exams. Whether a student has passed a course is decided by using the scores of midterm and final exams. If this student fails the course as a result of these exams, he can take a make-up exam of this course, and the score of the make-up exam is replaced with the final exam. However

Sarcasm is a common way of rhetoric in our daily life. It is used to express the opposite of the literal meaning, which makes it a challenging task in sentiment analysis of natural language processing (NLP). The formation mechanism of sarcasm is usually caused by the contrast between the positive sentiment and the negative situation. In this paper, we propose a contextual dual-view attention network

Latent factor models (LFMs) have been widely applied in many rating recommendation systems because of their prediction rating capability. Nevertheless, LFMs may not fully leverage rating information and lack good recommendation performance. Furthermore, many subsequent works have often used auxiliary text information, such as user attributes, to improve the prediction effect. However, they did not

In this paper, the design of a fractional-order hyperbolic adaptive neuro-fuzzy backstepping sliding mode controller (HANFBSMC) has been addressed for a class of fractional-order chaotic systems with time-varying delays in their states, control inputs, disturbances and uncertainties. In the proposed controller, adaptive rules are used both in a neuro-fuzzy estimator to estimate the unknown system dynamics

In the following study, an innovative domain adaptation technique is proposed. dCor based Domain Adaptation or dDA technique is based on maximum mean discrepancy (MMD) and distance correlation (dCor); a powerful yet general correlation measure which is applicable to arbitrary-dimensional random variables. By projecting the samples to a common latent feature space, dDA minimizes the discrepancy between

This research introduces a fusion architecture that integrates balanced scorecards (BSCs) and network data envelopment analysis (NDEA) to conduct a performance evaluation task from multiple perspectives. The architecture is able to capture the dynamics of production processes and sub-processes, uncover some of the components behind successful business practices, and shed light on needed actions for

Graph convolutional network (GCN) nowadays become new state-of-the-art for networks representation learning. Most of the existing methods are single-granular methods that failed to analyze the graph at multi-granular views so as to lose abundant information. Advanced graph pooling techniques can be successfully benefiting from semi-supervised networks representation learning. How to capture multi-granular

Network representation learning (NRL) maps vertices into latent vector space for further network inference. The existing algorithms concern more about whether the vectors of two similar nodes be close in latent vector space while the hierarchy proximity has been largely neglected by them. The distribution of the representation vectors needs to reflect the hierarchical structural properties which widely

With the increasing penetration of wind power in renewable energy systems, it is important to improve the accuracy of wind speed prediction. However, wind power generation has great uncertainties which make high-quality interval prediction a challenge. Existing multi-objective optimization interval prediction methods do not consider the robustness of the model. Thus, trained models for wind speed interval

In the literature, there have been numerous studies demonstrating experimentally that type-2 fuzzy models outperform their type-1 counterparts. Although the advantages of these models seem to be well justified, the quantification of the improvements is not carefully evaluated and critically assessed in the existing studies. A thorough multi-objective experimental numeric evaluation of benefits of type-2

Accurate classification and recognition of coal flotation froth is one of the key technologies for intelligent coal separation. At present, the coal flotation process relies on artificial recognition of froth features for adjusting the reagent dosage, which cannot realize the optimal control of the quality of the clean coal product and the cost of the reagents. Therefore, in this paper, it is proposed

Feature selection is an essential preprocessing requirement when solving a classification problem. In this respect, the Relief algorithm and its derivatives have been demonstrated to be a class of successful feature selectors. However, the computational cost of these algorithms is very high when large-scale datasets are processed. To solve this problem, we propose the fast ReliefF algorithm based on

Deep hashing has been widely applied in large scale image retrieval due to its high computation efficiency and retrieval performance. Recently, training deep hashing networks with a triplet ranking loss become a common framework. However, most of the triplet ranking loss based deep hashing methods cannot obtain satisfactory retrieval performance due to their ignoring the relative similarities among

This paper presents a deep learning-based machine translation (MT) system that translates a sentence of subject-object-verb (SOV) structured language into subject-verb-object (SVO) structured language. This system uses recurrent neural networks (RNNs) and Encodings. Encode embedded RNNs generate a set of numbers from the input sentence, where the second RNNs generate the output from these sets of numbers

Model Agnostic Meta Learning (MAML) has become the most representative meta learning algorithm to solve few-shot learning problems. This paper mainly discusses MAML framework, focusing on the key problem of solving few-shot learning through meta learning. However, MAML is sensitive to the base model for the inner loop, and training instability occur during the training process, resulting in an increase

Incremental concept cognitive learning is an emerging active issue that concerning incremental concept learning and dynamic knowledge processing in the dynamic context environments. As the development of that, how to measure the stability of a concept structure in the view of concept cognitive learning has become an urgent issue need to be solved. Motivated by that, we propose a method to analyze the

Data reduction processes are designed not only to reduce the amount of data, but also to reduce noise interference. In this study, we focus on researching sample reduction algorithms for the classification and regression data. A sample quality evaluation measure denoted by NN-kNN, which is inspired by human social behavior, is proposed. This measure is a local evaluation method that can accurately

In any multi-factor high-order fuzzy logical relationship (FLR) based forecasting model, a FLR reflects the influence of both the main factor (the forecasted factor) and all the influence factors on the main factor. Thus, the antecedent of a FLR includes multiple premises related to the main factor as well as all the influence factors. In real time series, there may exist another kind of influence:

The prediction of multiple numeric outputs at the same time is called multi-target regression (MTR), and it has gained attention during the last decades. This task is a challenging research topic in supervised learning because it poses additional difficulties to traditional single-target regression (STR), and many real-world problems involve the prediction of multiple targets at once. One of the most

Dealing with high-dimensional gene expression data is a challenging issue, and it is crucial to select multiple informative subsets of genes for cancer classification. In this regard, many statistical and machine learning methods with regulations have been developed. However, these methods neglected the epistasis, i.e., some genes may cover or affect other genes. In this article, we propose a fuzzy

Symmetry is present in nature and science. In image processing, kernels for spatial filtering possess some symmetry (e.g. Sobel operators, Gaussian, Laplacian). Convolutional layers in artificial feed-forward neural networks have typically considered the kernel weights without any constraint. We propose to investigate the impact of a symmetry constraint in convolutional layers for image classification

In the shipping industry, the sustainability of the shipping companies has received more attention besides the economic benefits. A suitable evaluation model can provide a credible sustainable improvement test and guide the operation and management of shipping companies. This study is concerned with a granular evaluation model for the sustainable competitiveness evaluation of container liners, where

It has been a challenging problem to identify species of butterflies, especially from images taken in natural environments. Therefore the First international butterfly species recognition competition was organized at the third Data Mining Competition in China in 2018, so as to find good solutions to this challenging problem. The baseline for the competition was based on the Faster R-CNN for it was

Most of the existing deep subspace clustering methods leverage convolutional autoencoders to obtain feature representation for non-linear data points. These methods commonly adopt the structure of a few convolutional layers because stacking many convolutional layers may cause computationally inefficient and optimization difficulties. However, long-range dependencies can hardly be captured when convolutional

Harris Hawks Optimization is a recently proposed algorithm inspired by the cooperative manner and chasing behavior of harris. However, from the experimental results, it can be noticed that HHO may fall in local optima or have a slow convergence curve in some complex optimization tasks. In this paper, an improved version of HHO called IHHO is proposed which enhances the performance of HHO by combining

Structural information is very important for improving the classification performance of classifiers. In order to increase the generalization performance of support vector machine (SVM) directly, several kinds of structured SVMs have been proposed. These algorithms with structural information only simply embed the global structural information or the local within-class information into SVM model. Thus

Big data is usually massive, diverse, time-varying, and high-dimensional. The focus of this paper is on the domain description of big data, which is the basis for solving the above problems. This paper has three main contributions. Firstly, one hyperellipsoid model is proposed to analyze domain description of big data. The parameters of the hyperellipsoid model can be adaptively adjusted according

Class imbalance problems are pervasive in many real-world applications, yet classifying imbalanced data remains to be a very challenging task in machine learning. SMOTE is the most influential oversampling approach. Based on SMOTE, many variants have been proposed. However, SMOTE and its variants have three drawbacks: (1) the probability distribution of the minority class samples is not considered;

For Multi-label classification, redundant and irrelevant features degrade the performance of classification. To select the best features based on several conflicting objectives, feature selection can be modeled as a large-scale optimization problem. However, most existing multi-objective feature selection methods select the features based on minimizing two well-known objectives, the number of features

Terms that occur too frequently or rarely in various texts are not useful for text classification. Pruning can be used to remove such irrelevant terms reducing the dimensionality of the feature space and, thus making feature selection more efficient and effective. Normally, pruning is achieved by manually setting threshold values. However, incorrect threshold values can result in the loss of many useful

Biomedical event extraction is an important branch of biomedical information extraction. Trigger extraction is the most essential sub-task in event extraction, which has been widely concerned. Existing trigger extraction studies are mostly based on conventional machine learning or neural networks. But they neglect the ambiguity of word representations and the insufficient feature extraction by shallow

The ensemble learning methods have always been paid attention to their successful performance in handling supervised classification problems. Nevertheless, some deficiencies, such as inadequate diversity between classifiers and existing redundant classifiers, are among the main challenges in this kind of learning. In recent years, a method called density peak has been used in clustering methods to

The crowd flows prediction plays an important role in urban planning management and urban public safety. Accuracy is a challenge for predicting the flow of crowds in a region. On the one hand, crowd flow is influenced by many factors such as holidays and weather. On the other hand, sample data about crowd flows are generally high-dimensional, which not only has a negative impact on the prediction accuracy

The spatial evolutionary game is one of the efficient models to explain the emergence and maintenance of cooperation among selfish individuals. In the existing work, the relationship between self-questioning dynamical evolutionary game model and Ising model has been discussed. However, the study on the dividing lines which were used to distinguish entirely cooperative phase, entirely defective phase

This paper explores how to construct an adaptive iteration learning control (AILC) mechanism for a class of discrete-time nonrepetitive systems subject to iteration-varying unknown parameters and unidentical initial condition. Firstly, for the linear discrete-time nonrepetitive systems, by minimizing the discrepancy of the real system outputs from the estimated system outputs, a gradient-type adaptation

Person re-identification (Re-ID) models usually present a limited performance when they are trained on one dataset and tested on another dataset due to the inter-dataset bias (e.g. completely different identities and backgrounds) and the intra-dataset difference (e.g. camera and pose changes). In other words, the absence of identity labels (who the person is) and pairwise labels (whether a pair of

Link prediction has received a great deal of attention from researchers. Most of the existing researches are based on the network topology but ignore the importance of its preference; for aggregating multiple pieces of information, they normally sum up them directly. In this paper, a path-based probabilistic model is proposed to estimate the potential connectivity between any two nodes. It takes carefully

The notion of an m-polar \((\in ,\) \(\in )\)-fuzzy a-ideal is introduced, and its properties are investigated. The relationship between m-polar fuzzy subalgebra, m-polar fuzzy ideal, and m-polar \((\in ,\) \(\in )\)-fuzzy a-ideal is examined. Conditions for an m-polar fuzzy ideal to be an m-polar \((\in ,\) \(\in )\)-fuzzy a-ideal are provided. The relationship between m-polar \((\in ,\) \(\in )\)-fuzzy

In this paper, we propose a two-stage temporal proposal algorithm for the action detection task of long untrimmed videos. In the first stage, we propose a novel prior-minor watershed algorithm for action proposals with precise prior watershed proposal algorithm and minor supplementary sliding window algorithm. Here, we propose the correctness discriminator to fill the proposals that watershed proposal

Formal concept analysis has shown much potential in supporting personalized learning. This paper is motivated by several application scenarios of concept lattices in supporting mathematics education. The aim of the paper is to develop a scheme for designing problem-chains based on concept lattices and provide evaluation methods towards steadiness of the corresponding learning processes. Particularly

The categorical clustering problem has attracted much attention especially in the last decades since many real world applications produce categorical data. The k-mode algorithm, proposed since 1998, and its multiple variants were widely used in this context. However, they suffer from a great limitation related to the update of the modes in each iteration. The mode in the last step of these algorithms

This paper is to study a mathematical model of computation for natural languages. Specifically, we enrich the L-fuzzy automata theory by using the concepts from L-fuzzy rough set theory so that new model, namely, L-fuzzy rough automata can handle both the concepts such as ambiguity and impreciseness arise in natural languages. Further, we study the determinization of an L-fuzzy rough automaton and

The continuous advancement of DDoS attack technology and an increasing number of IoT devices connected on 5G networks escalate the level of difficulty for DDoS mitigation. A growing number of researchers have started to utilise Deep Learning algorithms to improve the performance of DDoS mitigation systems. Real DDoS attack data has no labels, and hence, we present an intelligent attack mitigation (IAM)

The machine learning of small sample size is one of the most challenging problems in face recognition. Multiscale patch cooperative representation for face recognition provides multiple patch scales to a sample set. In each patch scale, all samples in the training set and test set are segmented into patches of the same size, followed by a collaborative representation classification. A sample belongs

Numerous works implemented multi-view clustering algorithms in document clustering. A challenging problem in document clustering is the similarity metric. Existing multi-view document clustering methods broadly utilized two measurements: the Cosine similarity (CS) and the Euclidean distance (ED). The first did not consider the magnitude difference (MD) between the two vectors. The second can’t register

Nowadays, non-negative matrix factorization (NMF) based cluster analysis for multi-view data shows impressive behavior in machine learning. Usually, multi-view data have complementary information from various views. The main concern behind the NMF is how to factorize the data to achieve a significant clustering solution from these complementary views. However, NMF does not focus to conserve the geometrical

Emergency schemes assessment (ESA) is a momentous activity for the country or government to improve emergency management, which can effectively reduce casualties and economic losses as much as possible. The choice of emergency scheme involves many quantitative or qualitative attributes, thus it can be viewed as a complicated multiple attribute decision making (MADM) issue. Whereas the business and

Time series prediction (TSP) is a process of using data collected at different times in the past for statistical analysis, so as to speculate on the trend of things, where the non-stationary and non-linear characteristics of data portray a hard setting for predictive tasks. Obviously, there will be no single model that could perform the best for all TSP issues. Dynamic Ensemble Selection (DES) technique

In the present paper, we consider a two-echelon supply chain problem including a manufacturer selling green products to a retailer, and then selling to customers. The market base, customer demand, production cost, and sensitivity to price are recognized as uncertain variables due to the influence of many human factors in reality. The aim is to maximize the entire profit of the supply chain, improve

Multi-view spectral clustering is a hot research area which has attracted increasing attention. Most existing multi-view spectral clustering methods utilize a two-step strategy. The first step obtains a common embedding by fusing spectral embeddings of different views, and the second step conducts hard clustering, such as K-means or spectral rotation, on the common embedding. Because the goal of the

Density peaks clustering (DPC) model focuses on searching density peaks and clustering data with arbitrary shapes for machine learning. However, it is difficult for DPC to select a cut-off distance in the calculation of a local density of points, and DPC easily ignores the cluster centers with lower density in datasets with variable densities. In addition, for clusters with complex shapes, DPC selects

In this paper, we introduce the notion of dynamic interval-valued neutrosophic soft sets (DIVNSSs) by embedding the time factor to interval-valued neutrosophic soft sets (IVNSSs). We also present some related set theoretic operations, such as complement, union, intersection, and-product, and or-product. Then, we propose the information measures of DIVNSSs, including the distance, similarity, and entropy

A classification problem aims at constructing a best classifier with the smallest risk. When the sample size approaches infinity, the learning algorithms for a classification problem are characterized by an asymptotical property, i.e., universal consistency. It plays a crucial role in measuring the construction of classification rules. A universal consistent algorithm ensures that the larger the sample

The main purpose of this paper is to provide a solution, through which one can efficiently reduce the population of cancer cells by injecting the lowest dose of the drug; therefore, reducing the side effects of the drug on healthy cells. In this paper, a mathematical model of stem Chronic Myelogenous Leukemia (CML) is used. To this aim, a hybrid method is used, that is a combination of the Eligibility

In this paper, a deep belief network (DBN) is employed to deal with the problem of the patient attendance disposal in accident & emergency (A&E) departments. The selection of the hyperparameters of the employed DBN is automated by using the particle swarm optimization (PSO) algorithm that is known for its simplicity, easy implementation and relatively fast convergence rate to a satisfactory solution