Article
Computer Science, Information Systems
Kai Zhang, Jianhua Dai
Summary: The TOPSIS method is a technique for sorting and classifying alternatives. This study introduces decision-theoretic rough fuzzy sets to expand the application scope of the TOPSIS method. A novel TOPSIS method is proposed that simultaneously handles sorting and classification using the principles of the TOPSIS method and decision-theoretic rough fuzzy sets. The method incorporates fuzzy concepts, defines decision areas and joint decision areas, and establishes sorting rules and decision processes.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Zhouming Ma, Jusheng Mi, Yiting Lin, Jinjin Li
Summary: Variable precision rough set (VPRS) has been widely studied as an essential way of knowledge representation and acquisition in uncertainty theory. This paper investigates the corresponding CVPRS model based on a covering-based rough set model, and systematically studies its algebraic structures and properties. An attribute reduction approach is proposed for a covering-based decision information system using the CVPRS model, and the performances of different boundary operators and related indices in these reduction methods are compared. Necessity rules and possibility rules extraction methods corresponding to decision classes are established, and their validity and security are theoretically verified.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Qian Zhou, Xiaojun Xie, Hua Dai, Weizhi Meng
Summary: Minimum vertex cover of hypergraphs is a variation of the widely studied minimum vertex covering problem. Existing algorithms for general graphs are not efficient enough for large-scale hypergraphs. To address this, we propose a novel rough set-based approach that combines rough set theory with stochastic local search algorithm. Experimental results show the advantages and limitations of our proposed approach.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Haibo Jiang, Bao Qing Hu
Summary: Decision-theoretic rough sets (DTRS) have been widely used in risk decision-making as a classic model of three-way decisions. In this paper, we propose a decision-theoretic fuzzy rough set (DTFRS) model in hesitant fuzzy information systems and discuss its application in multi-attribute decision-making (MADM). The established method not only considers decision risk, but also provides instructions on how to choose actions for each alternative and gives corresponding semantic explanations.
INFORMATION SCIENCES
(2021)
Article
Mathematics
Laifa Tao, Chao Wang, Yuan Jia, Ruzhi Zhou, Tong Zhang, Yiling Chen, Chen Lu, Mingliang Suo
Summary: A fault diagnosis strategy based on a novel rough set model is proposed to deal with the challenges of simultaneous faults in the satellite power system. By introducing a concise loss function matrix and fuzzy neighborhood relationship, a new rough set model called FN zeta DTRS accurately mines and characterizes the relationship between fault and data. An attribute rule-based fault matching strategy is designed to address the difficulty of obtaining and mapping simultaneous fault data. Experimental results demonstrate the effectiveness and superiority of the proposed approach.
Article
Computer Science, Information Systems
Xin Yang, Miaomiao Li, Hamido Fujita, Dun Liu, Tianrui Li
Summary: In this paper, a method for improving the performance of attribute reduction in a dynamic data environment is proposed. By combining incremental technology and accelerated reduction strategy, the method utilizes stable attribute groups and matrix-based incremental mechanisms for reduction search and dynamic attribute reduction. Experimental results demonstrate the effectiveness of the proposed method in terms of stability, computational cost, and classification accuracy.
INFORMATION SCIENCES
(2022)
Article
Mathematics
Zaibin Chang, Junchao Wei
Summary: Multigranulation rough set theory is an effective tool for data analysis and mining in multicriteria information systems. This article proposes novel methods to quickly compute the CMFRS models, which have been constructed through fuzzy beta-neighborhoods or multigranulation fuzzy measures. Matrix representations and operations are studied, and experiments are conducted to illustrate the effectiveness of the approaches.
JOURNAL OF MATHEMATICS
(2023)
Article
Computer Science, Information Systems
Jin Ye, Jianming Zhan, Bingzhen Sun
Summary: This study explores the application of three-way decision in multi-attribute decision-making and introduces a new method for three-way multi-attribute decision-making. By using a data-driven approach to determine relative loss functions and a new conditional probability calculation method, the study aims to address MADM problems with fuzzy values and incomplete data. The proposed TW-MADM method and corresponding MADM algorithm are shown to be feasible, effective, superior, and stable through comparative and experimental analysis. The method with optimistic strategies is demonstrated to be more viable and stable compared to compromise and pessimistic strategies.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Hua Mao, Shengyu Wang, Chang Liu, Gang Wang
Summary: Attribute reduction is a critical aspect of rough set theory in data analysis. Existing methods mainly focus on theories, leading to complexities in searching for attribute reducts. This paper proposes a visual method that overcomes this limitation and demonstrates its effectiveness in a conventional information system.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Xianyong Zhang, Jiefang Jiang
Summary: This study improves the Variable Precision Multigranulation Fuzzy Rough Sets (VP-MFRSs) by proposing Decision-Theoretic Multigranulation Fuzzy Rough Sets (DT-MFRSs) which systematically fuse the multigranulation maximum and minimum. DT-MFRSs provide tri-level analysis of measurement, modeling, and reduction via three-way decisions. The study extends and improves VP-MFRSs by introducing optimistic, pessimistic, and compromised models, and enhances uncertainty optimization through a new reduction criteria.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Gongao Qi, Bin Yang, Wei Li
Summary: In this paper, four types of fuzzy neighborhood operators based on fuzzy covering and their implicators are proposed. The equalities among overlap function-based fuzzy neighborhood operators on a finite fuzzy covering are investigated. The operators are divided into seventeen groups according to equivalence relations, and the partial order relations among them are discussed. Two types of neighborhood-related fuzzy covering-based rough set models are proposed, and the groups and partial order relations are also discussed. A novel fuzzy TOPSIS methodology is applied to solve a biosynthetic nanomaterials select issue, and its rationality and enforceability are verified by comparing with nine different methods.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Mengmeng Li, Chiping Zhang, Minghao Chen, Weihua Xu
Summary: This paper proposes two pairs of multigranulation double-quantitative decision-theoretic rough sets models to address the issue of incomplete reflection of relative and absolute information in traditional models. Through the study of decision rules and the inner relationship between these models, the effectiveness and superiority of the proposed models are further demonstrated.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Jiali He, Liangdong Qu, Zhihong Wang, Yiying Chen, Damei Luo, Ching-Feng Wen
Summary: This paper investigates attribute reduction in an incomplete categorical decision information system (ICDIS) based on fuzzy rough sets. An attribute reduction algorithm is proposed and experiments show that it outperforms existing algorithms.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Artificial Intelligence
Guoping Lin, Linlin Xie, Jinjin Li, Jinkun Chen, Yi Kou
Summary: This paper introduces an important expanded quantification fuzzy rough set model, the local double quantitative fuzzy rough set model over two universes, which is used to measure the relative quantitative information between fuzzy similarity classes and basic concepts. It addresses the issue of existing models ignoring the absolute quantitative information in the fuzzy information system. The properties, decision rules, and an effective reduction method of the model are studied, and experimental comparisons demonstrate its computational efficiency and approximate accuracy in concept approximation and reduction.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Guoqiang Wang, Tianrui Li, Pengfei Zhang, Qianqian Huang, Hongmei Chen
Summary: Local rough set models are effective for handling large data sets with small amounts of labeled data, improving computational performance significantly. The double-local rough set framework introduces the concept of local equivalence classes and defines lower deletion matrix, upper addition matrix, and upper deletion matrix. Proposed algorithms in double-local rough sets outperform original counterparts in attribute reduction and knowledge discovery.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Lingxiang Yao, Worapan Kusakunniran, Qiang Wu, Jian Zhang, Zhenmin Tang, Wankou Yang
Summary: This paper introduces a new model-based representation SGEI and a hybrid representation for gait recognition, which enhance the robustness of gait recognition in various environments.
PATTERN RECOGNITION LETTERS
(2021)
Article
Computer Science, Artificial Intelligence
Zeren Sun, Yazhou Yao, Jimin Xiao, Lei Zhang, Jian Zhang, Zhenmin Tang
Summary: This paper presents a framework that resolves visual polysemy by dynamically matching text queries with images, removing outliers and learning classification models. Extensive experiments on the CMU-Poly-30 and MIT-ISD datasets demonstrate the effectiveness of the proposed approach in addressing the issue of visual polysemy.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Information Systems
Huafeng Liu, Chuanyi Zhang, Yazhou Yao, Xiu-Shen Wei, Fumin Shen, Zhenmin Tang, Jian Zhang
Summary: This paper introduces a novel approach for removing irrelevant samples from real-world web images during training, while using useful hard examples to update the network, in order to achieve better performance in fine-grained recognition. This approach demonstrates superior results compared to current state-of-the-art web-supervised methods through extensive experiments on three commonly used fine-grained datasets.
IEEE TRANSACTIONS ON MULTIMEDIA
(2022)
Article
Computer Science, Information Systems
Tao Chen, Guo-Sen Xie, Yazhou Yao, Qiong Wang, Fumin Shen, Zhenmin Tang, Jian Zhang
Summary: In this paper, a one-shot semantic image segmentation method is proposed that leverages multi-class information. Episodic training strategy, pyramid feature fusion module, and self-prototype guidance branch are introduced to improve segmentation accuracy and robustness. Experimental results demonstrate the superiority of this method.
IEEE TRANSACTIONS ON MULTIMEDIA
(2022)
Article
Computer Science, Information Systems
Yimeng Zhang, Xiuyi Jia, Zhenmin Tang
Summary: This paper addresses the uncertainty measurement problem in interval-set decision tables by introducing similarity relation and granular structure to measure uncertainty, and then designing two extended uncertainty measures. An alternative uncertainty measure based on conditional information entropy, as well as a corresponding attribute reduction algorithm, is proposed. Experimental results demonstrate the effectiveness and suitability of the proposed uncertainty measures for interval-set decision tables.
INFORMATION SCIENCES
(2021)
Article
Engineering, Electrical & Electronic
Haonan Luo, Guosheng Lin, Yazhou Yao, Zhenmin Tang, Qingyao Wu, Xiansheng Hua
Summary: Existing action recognition methods often suffer from low accuracy when dealing with complex backgrounds and activities. This paper proposes a deep architecture called Dense Semantics-Assisted Convolutional Neural Networks (DSA-CNNs) to effectively utilize dense semantic information of videos. The method, which employs bottom-up attention mechanism in the spatial stream and branch fusion in the temporal stream, shows competitive performance and outperforms other methods that utilize extra information for action recognition.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2022)
Article
Mathematics
Yuhua Ding, Zhenmin Tang, Fei Wang
Summary: This paper presents a single-sample face recognition method based on a shared generative adversarial network. By generating and expanding the gallery dataset, and utilizing a deep convolutional neural network for feature extraction and classifier training, it can effectively recognize single-sample faces.
Article
Mathematics
Qingze Yin, Guan'an Wang, Jinlin Wu, Haonan Luo, Zhenmin Tang
Summary: In this paper, a training strategy called TSDRC is proposed to address the problem of poor generalization ability to invisible people in person re-identification. It transfers knowledge between source and target domains, and applies dynamic weighting and triplet loss to improve the performance of unsupervised domain adaptability. The experiments demonstrate that the proposed method significantly improves the performance of unsupervised domain adaptability.
Article
Computer Science, Artificial Intelligence
Yi Wang, Xiaobo Shen, Zhenmin Tang, Ming Zhang
Summary: This study proposes a semi-supervised semi-paired deep hashing method named SPSDH for large-scale data, aiming to address the lack of paired and labelled information in cross-view retrieval tasks. Experimental results demonstrate the superior performance of SPSDH in semi-supervised semi-paired retrieval tasks.
IET COMPUTER VISION
(2022)
Article
Computer Science, Artificial Intelligence
Qingze Yin, Guan'an Wang, Guodong Ding, Qilei Li, Shaogang Gong, Zhenmin Tang
Summary: This study proposes a novel Sub-space Consistency Regularization (SCR) algorithm that can speed up the ReID procedure by 0.25 times while maintaining competitive accuracy. SCR transforms real-value features into short binary codes and uses clustered centroids to calculate distances, striking a balance between speed and accuracy.
NEURAL PROCESSING LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Zhenhuang Cai, Guo-Sen Xie, Xingguo Huang, Dan Huang, Yazhou Yao, Zhenmin Tang
Summary: This paper proposes a simple yet effective method named MS-DeJOR for training robust models in the presence of web noise in deep neural networks. Unlike existing methods, MS-DeJOR decouples sample selection from the training procedure, uses a negative entropy term to prevent false positives from being overemphasized, and leverages accumulated predictions to refurbish noisy labels and re-weight training images for improved performance.
PATTERN RECOGNITION
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Gensheng Pei, Fumin Shen, Yazhou Yao, Guo-Sen Xie, Zhenmin Tang, Jinhui Tang
Summary: Optical flow is a valuable cue for unsupervised video object segmentation. We propose a hierarchical feature alignment network (HFAN) that effectively aligns appearance and motion features for segmenting target objects.
COMPUTER VISION, ECCV 2022, PT XXXIV
(2022)
Proceedings Paper
Computer Science, Software Engineering
Guhua Chen, Gensheng Pei, Yin Tang, Tao Chen, Zhenmin Tang
Summary: Data augmentation is widely used in computer vision tasks, but its application to oriented object detection in remote sensing images is challenging. This work proposes a multi-sample data augmentation method called SSMup, which integrates Mosaic, Mixup, and SSMOTE to evenly distribute target objects in augmented samples and provide rich background information. The proposed method significantly improves detection and generalization performance in remote sensing images compared to existing state-of-the-art methods.
2022 IEEE 24TH INTERNATIONAL WORKSHOP ON MULTIMEDIA SIGNAL PROCESSING (MMSP)
(2022)
Article
Engineering, Electrical & Electronic
Qingze Yin, Guan'an Wang, Guodong Ding, Shaogang Gong, Zhenmin Tang
Summary: Various supervised and unsupervised ReID methods face limited training data labels and noisy labels. By proposing to learn pseudo-patch-labels, the issue is effectively alleviated, leading to significant performance advantages in experiments.
IEEE SIGNAL PROCESSING LETTERS
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Chuanyi Zhang, Yazhou Yao, Huafeng Liu, Guo-Sen Xie, Xiangbo Shu, Tianfei Zhou, Zheng Zhang, Fumin Shen, Zhenmin Tang
THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE
(2020)
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)
