Article
Computer Science, Information Systems
Wanfu Gao, Hanlin Pan
Summary: Multi-label feature selection is important for addressing multi-label data with high-dimensional features. Existing methods calculate label correlations differently, resulting in different label importance. Our proposed method overcomes the limitations of previous schemes by using mutual information to capture different cores of label sets instead of calculating label importance. Experimental results show that our method achieves the best classification performance among all multi-label feature selection methods.
Article
Physics, Multidisciplinary
Lingbo Gao, Yiqiang Wang, Yonghao Li, Ping Zhang, Liang Hu
Summary: A novel feature selection method, combining three aspects of candidate features, selected features, and label correlations, is proposed in this paper to evaluate feature relevance. By reducing unnecessary redundancy, the method is able to better capture the optimal features and outperform other state-of-the-art multi-label approaches in experiments.
Article
Computer Science, Hardware & Architecture
Sadegh Eskandari
Summary: Multi-label learning deals with data in which an instance may belong to multiple class labels simultaneously. The main problem of the existing multi-label feature selection algorithms is their inability to consider all possible subsets of feature space. This paper proposes a new method to address higher-order relevance and redundancy analysis and experimental results demonstrate its superiority over seven state-of-the-art multi-label feature selection algorithms.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Ping Zhang, Guixia Liu, Wanfu Gao, Jiazhi Song
Summary: This study introduces two new multi-label feature selection methods, LSMFS and MLSMFS, which extract all supplementary information and the maximum supplementary information from other labels, respectively. These methods aim to address the different effects and dynamic changes of label relationships, and experiments demonstrate their effectiveness against nine other methods.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Ping Zhang, Wanfu Gao
Summary: This study proposes a new method for feature selection in multi-label learning, which takes into account the influence of already-selected features and label correlations on feature relevance. The proposed method, Double Conditional Relevance-Multi-label Feature Selection (DCR-MFS), outperforms existing methods according to experimental results and theoretical analysis.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Information Systems
Wenbin Qian, Qianzhi Ye, Yihui Li, Jintao Huang, Shiming Dai
Summary: This paper proposes a relevance-based label distribution feature selection method, aiming to reduce model complexity and computational cost, as well as enhance the generalization ability of the learning model. By considering the relevance between features and labels, a convex optimization function is used for feature selection, and the Pearson correlation coefficient is used to describe the correlation information between labels. Experimental results demonstrate the effectiveness of the proposed method compared to existing methods in multiple evaluation metrics.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Ping Zhang, Wanfu Gao, Juncheng Hu, Yonghao Li
Summary: Feature selection is crucial in machine learning and data mining, with traditional methods being improved upon by the novel CWJR-FS method, which utilizes conditional-weight joint relevance to design a new feature relevancy term, outperforming other methods in experiments.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Information Systems
Yong Yang, Hongmei Chen, Yong Mi, Chuan Luo, Shi-Jinn Horng, Tianrui Li
Summary: This study proposes a multi-label feature selection method based on stable label relevance and label-specific features, which can efficiently handle large amounts of multi-label data and improve the classification performance.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Ping Zhang, Jiyao Sheng, Wanfu Gao, Juncheng Hu, Yonghao Li
Summary: This study proposes a multi-label feature selection method based on information theory, categorizing labels into two groups based on remaining uncertainty and utilizing relevancy ratio and weighted feature relevancy to evaluate candidate features. Experiment results show the effectiveness of the proposed method on real-world data sets.
Article
Computer Science, Artificial Intelligence
Ping Zhang, Guixia Liu, Jiazhi Song
Summary: This study proposes a multi-label feature selection method to capture a reliable and informative feature subset from high-dimensional multi-label data, which is important for pattern recognition.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Dianlong You, Yang Wang, Jiawei Xiao, Yaojin Lin, Maosheng Pan, Zhen Chen, Limin Shen, Xindong Wu
Summary: This paper proposes a novel online multi-label streaming feature selection scheme, taking into account the correlation between labels, and achieves significant advantages in terms of effectiveness and efficiency in performance evaluations.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Wanfu Gao, Pingting Hao, Yang Wu, Ping Zhang
Summary: The approximate approximation of low-order information-theoretic terms has been successful in addressing high-dimensional multi-label data. However, there are three critical issues in these approaches: (1) they are biased towards specific scenes due to single heuristic variable correlation assumption, (2) high-order variable correlations are ignored, and (3) the abundance of approaches confuses researchers. To address these issues, two types of probability distribution assumption are derived based on low-order variable correlations, and a unified feature selection framework called STFS is proposed. STFS includes high-order variable correlations and many previous approaches can be reduced to special forms of STFS. Extensive experiments demonstrate the superiority of STFS in classification.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Azar Rafie, Parham Moradi, Abdulbaghi Ghaderzadeh
Summary: Multi-label classification methods assign more than one label to each instance. High dimensional problems can reduce the performance of machine learning methods, so feature selection is introduced to choose a small set of prominent features. Traditional multi-label feature selection methods fail when applied on data streams, so online methods are introduced. This paper proposes a multi-objective search strategy using mutual information and Pareto optimal set theories to select streaming features, with the obtained results demonstrating its effectiveness.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Jinghua Liu, Songwei Yang, Yaojin Lin, Chenxi Wang, Cheng Wang, Jixiang Du
Summary: Multi-label feature selection is an important task in processing multi-semantic high-dimensional data. This paper proposes a new algorithm called FSEP, which considers label significance and pairwise label correlations. The proposed method achieves encouraging results compared with state-of-the-art MFS algorithms, as demonstrated by extensive experiments.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Yuling Fan, Baihua Chen, Weiqin Huang, Jinghua Liu, Wei Weng, Weiyao Lan
Summary: The task of multi-label feature selection (MLFS) is to reduce redundant information and generate the optimal feature subset from the original multi-label data. Existing methods either explore label correlations using pseudo label matrix or consider feature redundancy using information theory technique, but no prior literature unites them in a framework to perform feature selection. Therefore, a novel MLFS method called LFFS is proposed based on label correlations and feature redundancy. Experimental results demonstrate the effectiveness and superiority of the LFFS method among ten competition methods.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Physics, Multidisciplinary
Ping Zhang, Wanfu Gao, Juncheng Hu, Yonghao Li
Article
Computer Science, Artificial Intelligence
Liang Hu, Yonghao Li, Wanfu Gao, Ping Zhang, Juncheng Hu
PATTERN RECOGNITION
(2020)
Article
Computer Science, Artificial Intelligence
Juncheng Hu, Yonghao Li, Wanfu Gao, Ping Zhang
KNOWLEDGE-BASED SYSTEMS
(2020)
Article
Computer Science, Information Systems
Ping Zhang, Wanfu Gao, Juncheng Hu, Yonghao Li
Summary: A multi-label feature selection method based on label spectral clustering was proposed, which identifies important features and constructs feature subsets by clustering labels, and experimental results showed its superiority over seven existing multi-label feature selection methods.
INFORMATION SCIENCES
(2021)
Article
Automation & Control Systems
Ping Zhang, Wanfu Gao, Juncheng Hu, Yonghao Li
Summary: Feature selection is crucial in machine learning and data mining, with traditional methods being improved upon by the novel CWJR-FS method, which utilizes conditional-weight joint relevance to design a new feature relevancy term, outperforming other methods in experiments.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Physics, Multidisciplinary
Lingbo Gao, Yiqiang Wang, Yonghao Li, Ping Zhang, Liang Hu
Summary: A novel feature selection method, combining three aspects of candidate features, selected features, and label correlations, is proposed in this paper to evaluate feature relevance. By reducing unnecessary redundancy, the method is able to better capture the optimal features and outperform other state-of-the-art multi-label approaches in experiments.
Article
Computer Science, Artificial Intelligence
Juncheng Hu, Yonghao Li, Gaochao Xu, Wanfu Gao
Summary: This paper proposes a new multi-label feature selection method DSMFS, which achieves high-quality label subspace through dynamic subspace and dual-graph regularization, with experimental results demonstrating its superiority.
Article
Computer Science, Artificial Intelligence
Ping Zhang, Jiyao Sheng, Wanfu Gao, Juncheng Hu, Yonghao Li
Summary: This study proposes a multi-label feature selection method based on information theory, categorizing labels into two groups based on remaining uncertainty and utilizing relevancy ratio and weighted feature relevancy to evaluate candidate features. Experiment results show the effectiveness of the proposed method on real-world data sets.
Article
Computer Science, Information Systems
Yonghao Li, Liang Hu, Wanfu Gao
Summary: This paper proposes a robust multi-label feature selection method that considers both types of label correlations. It eliminates redundant and noisy information through a self-expression model and a regularizer, and designs an optimization scheme to handle the objective function.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Yonghao Li, Juncheng Hu, Wanfu Gao
Summary: This paper proposes a multi-label feature selection method named RLEFS, which improves the classification performance of models by considering the relationship between feature sets and label sets, as well as the importance of labels.
KNOWLEDGE AND INFORMATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yonghao Li, Liang Hu, Wanfu Gao
Summary: In recent years, joint feature selection and multi-label learning have been widely studied. However, existing multi-label feature selection methods face three challenges: neglecting feature redundancy, using low-quality graphs to capture local label correlations, and considering only either local or global label correlations. To address these challenges, we propose a method that preserves global and dynamic local label correlations by preserving the graph structure. We also introduce regularization terms to select low redundant features. Experimental results demonstrate the superiority of our method in classification tasks.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Yonghao Li, Liang Hu, Wanfu Gao
Summary: Multi-label feature selection is an efficient technique for dealing with high-dimensional multi-label data, but existing methods suffer from low feature discrimination and redundancy. This paper proposes a new regularization norm and optimization framework to address these issues, and empirical studies demonstrate the effectiveness and efficiency of the proposed method.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Wanfu Gao, Yonghao Li, Liang Hu
Summary: When dealing with high-dimensional multilabel data, we propose a feature selection method that shares latent feature and label structure. By designing an LSS term to share and preserve the latent structure, and employing graph regularization technique to ensure consistency, we achieve better results on multiple evaluation criteria according to experiments.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Information Systems
Wanfu Gao, Juncheng Hu, Yonghao Li, Ping Zhang
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
