Article
Computer Science, Information Systems
Lin Sun, Tianxiang Wang, Weiping Ding, Jiucheng Xu, Yaojin Lin
Summary: A novel feature selection method is proposed in this study using Fisher score and multilabel neighborhood rough sets to identify label correlations, automatically select neighborhood radius, and reduce complexity of multilabel data, leading to improved classification performance.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Lin Sun, Yusheng Chen, Weiping Ding, Jiucheng Xu, Yuanyuan Ma
Summary: This article proposes a novel adaptive fuzzy neighborhood-based multilabel feature subset selection approach with ant colony optimization (ACO) for multilabel classification. It addresses the issue of ignoring correlations among labels and the manual setting of neighborhood radius in existing feature selection models. The approach combines feature cosine similarity and label Jaccard similarity to effectively reflect overall similarity between samples, and utilizes dynamic adjustment coefficients to control label similarity importance. Experimental results demonstrate the effectiveness of the proposed algorithm in achieving excellent feature subset for multilabel classification.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Lin Sun, Tengyu Yin, Weiping Ding, Yuhua Qian, Jiucheng Xu
Summary: This article presents a feature selection method based on multilabel fuzzy neighborhood rough sets and maximum relevance minimum redundancy for multilabel data with missing labels. Experiments verify the effectiveness of the method in recovering missing labels and selecting significant features.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Information Systems
Tengyu Yin, Hongmei Chen, Zhong Yuan, Tianrui Li, Keyu Liu
Summary: Feature selection is important in multilabel learning, and fuzzy rough set theory is widely used in this field. This study focuses on the noise-tolerant fuzzy neighborhood rough set model and its feature selection strategy for multilabel learning. A parameterized hybrid fuzzy similarity relation is introduced to granulate multilabel data, and a noise-resistant feature selection algorithm is proposed.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Tomasz Klonecki, Pawel Teisseyre, Jaesung Lee
Summary: Feature selection is crucial in multi-label classification for building predictive models. Existing methods often disregard cost information associated with considered features. We address the problem of cost-constrained multilabel feature selection, aiming to select a feature subset relevant to multiple labels while adhering to a user-defined budget. Our approach ensures high predictive power without exceeding the specified budget per prediction. We propose a novel criterion combining relevance and cost for feature selection, along with an effective method for determining the trade-off between relevancy and cost. Experimental results demonstrate the superiority of our method over conventional methods on multilabel datasets.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoling Yang, Hongmei Chen, Tianrui Li, Jihong Wan, Binbin Sang
Summary: This paper introduces a novel neighborhood rough set Model based on Distance metric learning (NMD) to improve the discriminative ability and reduce uncertainty in representation. Experimental results demonstrate the effectiveness and superiority of the proposed feature selection algorithms on real-world datasets.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Zhihong Wang, Hongmei Chen, Zhong Yuan, Xiaoling Yang, Pengfei Zhang, Tianrui Li
Summary: This paper extends rough entropy to fuzzy rough set theory and proposes the fuzzy rough entropy in fuzzy approximate space. The concepts of fuzzy joint rough entropy, fuzzy conditional rough entropy, and fuzzy rough mutual information are defined. By constructing inner and outer significance functions based on fuzzy rough mutual information, a feature selection algorithm is designed that can handle various types of data and effectively delete redundant features. The experimental results demonstrate the adaptability and effectiveness of the proposed method.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Chuan Luo, Hong Pi, Tianrui Li, Hongmei Chen, Yanyong Huang
Summary: This paper proposes a novel fuzzy rank discrimination measure for feature selection in ordinal datasets. The proposed measure satisfies the monotonicity property and can obtain the optimal feature subset without explicitly evaluating all possible feature combinations. Experimental results demonstrate the superiority of this method in monotonic feature selection.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Theory & Methods
Tengyu Yin, Hongmei Chen, Tianrui Li, Zhong Yuan, Chuan Luo
Summary: This paper investigates the use of soft labels for label enhancement in multilabel feature selection. By constructing a robust fuzzy neighborhood and utilizing a label enhancement strategy, the accuracy of feature selection in multilabel data can be improved. The research results demonstrate the good performance of this method in terms of classification performance and anti-noise ability.
FUZZY SETS AND SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoya Che, Degang Chen, Jusheng Mi
Summary: This article proposes the instance-level label correlation distribution based on fuzzy rough set theory and applies it to design a novel multilabel learner. The local importance of features to the label is quantitatively analyzed for each multilabel instance, and the instance-level label correlation is constructed. The instance-level label correlation distribution is integrated with the empirical label relevance to define the constraints of the optimization function, and the position of subseparating hyperplanes in the input space is quantitatively characterized to reduce the complexity of the multilabel classifier and improve learning performance.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Pawel Teisseyre, Jaesung Lee
Summary: In this paper, a multilabel all-relevant feature selection task is discussed for multi-label classification. The all-relevant methods aim to identify all features related to target labels, including weakly relevant features. The paper proposes a relevancy score calculation method based on conditional mutual information and a testing procedure for separating relevant and irrelevant features.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Lin Sun, Jiuxiao Zhang, Weiping Ding, Jiucheng Xu
Summary: This paper presents a mixed measure-based feature selection method using Fisher score and neighborhood rough sets (NRS) model to address the limitations of existing methods. Experimental results show that the developed algorithm is effective and achieves high classification accuracy.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Francisco Macedo, Rui Valadas, Eunice Carrasquinha, M. Rosario Oliveira, Antonio Pacheco
Summary: Feature selection is an essential preprocessing technique to improve regression and classification tasks by reducing dimensionality. DMIM method, based on Decomposed Mutual Information Maximization, overcomes complementarity penalization issue in existing methods, showing better classification performance in experiments.
Article
Computer Science, Artificial Intelligence
Shuangjie Li, Kaixiang Zhang, Yali Li, Shuqin Wang, Shaoqiang Zhang
Summary: Feature selection is crucial in many fields, especially in machine learning. The proposed method OFS-Gapknn effectively addresses the challenges of online streaming features by defining a new neighborhood rough set relation and analyzing relevance and redundancy features. Experimental results demonstrate the dominance and significance of this method.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Jiucheng Xu, Kaili Shen, Lin Sun
Summary: This article proposes a multi-label feature selection method based on fuzzy neighborhood rough set. By combining the feature importance evaluation methods from information view and algebra view, the problems of existing methods in dealing with mixed data are addressed.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Physics, Multidisciplinary
Shiguang Zhang, Ting Zhou, Lin Sun, Wei Wang, Baofang Chang
Article
Computer Science, Artificial Intelligence
Zhan'ao Xue, Liping Zhao, Lin Sun, Min Zhang, Tianyu Xue
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2020)
Article
Computer Science, Artificial Intelligence
Lin Sun, Lanying Wang, Weiping Ding, Yuhua Qian, Jiucheng Xu
Summary: The article introduces a feature selection method based on FNMRS for preprocessing data and improving its classification performance in heterogeneous data sets. The approach constructs uncertainty measures using fuzzy neighborhood rough sets and neighborhood multigranulation rough sets, and provides optimistic and pessimistic FNMRS models along with fuzzy neighborhood entropy-based uncertainty measures. Additionally, the Fisher score model is utilized to reduce the complexity of high-dimensional data sets by deleting irrelevant features and a forward feature selection algorithm is presented to enhance the performance of heterogeneous data classification.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Lin Sun, Shanshan Si, Jing Zhao, Jiucheng Xu, Yaojin Lin, Zhiying Lv
Summary: This paper proposes two mechanisms for improving the binary Monarch Butterfly Optimization (BMBO) algorithm in metaheuristic feature selection. The new mechanisms include the introduction of transfer functions to convert continuous space into binary and the design of two BMBO models based on these transfer functions. Additionally, a new step length parameter is proposed to update the position of the butterfly, and local disturbance and group division strategies are added to prevent the algorithm from falling into local optima. Experimental results show that the designed algorithm has great classification efficiency compared to other related technologies.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Lin Sun, Tengyu Yin, Weiping Ding, Yuhua Qian, Jiucheng Xu
Summary: This article presents a feature selection method based on multilabel fuzzy neighborhood rough sets and maximum relevance minimum redundancy for multilabel data with missing labels. Experiments verify the effectiveness of the method in recovering missing labels and selecting significant features.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Lin Sun, Tianxiang Wang, Weiping Ding, Jiucheng Xu, Anhui Tan
Summary: This paper presents a neighborhood-based multilabel classification method for dealing with missing labels in real-world multilabel data. By defining the neighborhood radius, restoring missing feature values, and investigating the fuzzy similarity relationship among samples, the classification performance of data with missing labels is improved.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Information Systems
Lin Sun, Mengmeng Li, Weiping Ding, En Zhang, Xiaoxia Mu, Jiucheng Xu
Summary: This paper proposes a novel adaptive fuzzy neighborhood-based feature selection method for imbalanced data with adaptive synthetic over-sampling. It addresses the limitations of manually setting fuzzy neighborhood radius and potential ignorance of boundary regions, and achieves effective classification results.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Lin Sun, Xinya Wang, Weiping Ding, Jiucheng Xu
Summary: This study developed a two-stage feature reduction model using fuzzy neighborhood rough sets and the binary whale optimization algorithm to address challenges in imbalanced data classification. Experimental results demonstrated the efficiency of the proposed algorithm for two-class and multiclass datasets.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Lin Sun, Yusheng Chen, Weiping Ding, Jiucheng Xu, Yuanyuan Ma
Summary: This article proposes a novel adaptive fuzzy neighborhood-based multilabel feature subset selection approach with ant colony optimization (ACO) for multilabel classification. It addresses the issue of ignoring correlations among labels and the manual setting of neighborhood radius in existing feature selection models. The approach combines feature cosine similarity and label Jaccard similarity to effectively reflect overall similarity between samples, and utilizes dynamic adjustment coefficients to control label similarity importance. Experimental results demonstrate the effectiveness of the proposed algorithm in achieving excellent feature subset for multilabel classification.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Lin Sun, Shanshan Si, Weiping Ding, Xinya Wang, Jiucheng Xu
Summary: This paper proposes a multiobjective sparrow search feature selection approach to address the challenges of balancing convergence and diversity in nondominated solutions. The approach combines the updating formula of observers with the mutualism phase of the symbiotic organisms search algorithm to improve the search ability. The paper also introduces sparrow ranking, feature ranking, and a preference information-based mutation algorithm to enhance the diversity of solutions and guide the population towards better solutions.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Lin Sun, Tianxiang Wang, Weiping Ding, Jiucheng Xu
Summary: This study develops a novel Partial Multilabel Learning (PML) model that addresses some issues in traditional PML models by introducing fuzzy neighborhood-based ball clustering and kernel extreme learning machine (KELM). The model preprocesses the data with ball k-means clustering, designs a new ball clustering model, develops the particle-ball fusion strategy, studies fuzzy membership functions and label enhancement, and constructs a nonsmooth convex objective function. Experimental results on 14 datasets confirm the effectiveness of the proposed algorithm.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Lin Sun, Shanshan Si, Weiping Ding, Xinya Wang, Jiucheng Xu
Summary: This study proposes a new feature subset selection scheme to deal with imbalanced data by fusing fuzzy multi-neighborhood rough set (FMRS) and binary whale optimization algorithm (BWOA). The method evaluates the distribution of different features using the standard deviation coefficient and constructs a fuzzy multi-neighborhood radius set. It also introduces fuzzy multi-neighborhood granule and fuzzy mem-bership degree to establish FMRS, and develops a feature significance measure to balance the properties and influences of different features. Experimental results demonstrate the effectiveness of the proposed algorithm for classification of imbalanced data.
INFORMATION FUSION
(2023)
Article
Computer Science, Information Systems
Shiguang Zhang, Chao Liu, Ting Zhou, Lin Sun
Article
Computer Science, Information Systems
Enhui Shi, Lin Sun, Jiucheng Xu, Shiguang 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)