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, Artificial Intelligence
Zhanao Xue, Bingxin Sun, Haodong Hou, Wenli Pang, Yanna Zhang
Summary: This article proposes intuitionistic hesitant fuzzy sets and multi-granulation rough intuitionistic hesitant fuzzy set models, and establishes three-way decision models. The research results show that these models can effectively evaluate objects with different attitudes and provide decision-making solutions.
COGNITIVE COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Wei Li, Bin Yang
Summary: As a generalization of equivalent class, fuzzy covering makes descriptions of the objective world more realistic, practical and accurate in some cases. In this paper, we establish four kinds of fuzzy probabilistic covering-based rough set, which combine the fuzzy covering-based neighborhood operator and the fuzzy probabilistic rough set, and then obtain the result of their three-way decision.
APPLIED SOFT COMPUTING
(2023)
Article
Mathematics
Wajid Ali, Tanzeela Shaheen, Iftikhar Ul Haq, Hamza Ghazanfar Toor, Tmader Alballa, Hamiden Abd El-Wahed Khalifa
Summary: Intuitionistic fuzzy information is a powerful tool for medical diagnosis applications to handle imprecise and uncertain data. Power aggregation operators can effectively combine different information sources, including expert opinions and patient data, to achieve more accurate diagnoses. This paper presents a novel approach utilizing decision-theoretic rough sets and power aggregation operators for the three-way decision model, aiming to manage vague and uncertain data in medical diagnosis and improve diagnostic accuracy. The proposed approach is validated through a medical case study, demonstrating its effectiveness in combining diverse information sources and improving patient outcomes.
Article
Mathematics, Applied
Wajid Ali, Tanzeela Shaheen, Hamza Ghazanfar Toor, Tmader Alballa, Alhanouf Alburaikan, Hamiden Abd El-Wahed Khalifa
Summary: The Decision-Theoretic Rough Set model offers a broader scope of applicability in rough sets by employing Bayesian theory to delineate regions of minimal risk. This study presents an enhanced iteration of the framework called GI-DTRS, which combines Decision-Theoretic Rough Sets with intuitionistic fuzzy sets. The incorporation of a tailored error function for intuitionistic fuzzy sets and the construction of similarity classes based on similarity measures contribute to its innovation. The practical efficacy of the proposed approach is demonstrated through a concrete experiment in the information technology domain, with a comprehensive comparative analysis conducted against existing techniques.
Article
Computer Science, Artificial Intelligence
Fang Liu, Yi Liu, Saleem Abdullah
Summary: This paper introduces the q-rung orthopair fuzzy numbers (q-ROFNs) to provide a method for solving multi-attribute decision making problems by combining Cq-ROFRS with the loss function matrix of DTRS. The aim is to improve the ability to solve uncertainties and ambiguities in decision making problems, and verify the feasibility of the methods through an algorithm.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Mathematics
R. Mareay, Ibrahim Noaman, Radwan Abu-Gdairi, M. Badr
Summary: This study introduces three models of intuitionistic fuzzy set approximation space based on covering, and proves the definitions and features using the notion of the neighborhood.
Article
Computer Science, Artificial Intelligence
Xin Yang, Yujie Li, Dun Liu, Tianrui Li
Summary: The approximation learning of fuzzy concepts associated with fuzzy rough sets and three-way decisions is an important technology for handling uncertain knowledge. This article explores the connection and interplay between fuzzy rough approximations and three-way approximations, proposing a hierarchical fuzzy rough approximation method in a dynamic fuzzy open-world environment. The article also discusses the interpretation and representation of fuzzy three-way regions in fuzzy rough sets. Experimental results demonstrate the effectiveness of the proposed hierarchical approximation learning models.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Chemistry, Multidisciplinary
Wajid Ali, Tanzeela Shaheen, Hamza Ghazanfar Toor, Faraz Akram, Md. Zia Uddin, Mohammad Mehedi Hassan
Summary: In today's fast-paced business environment, investment decision making has become more complex due to the uncertainty and ambiguity of financial data. Traditional decision-making models are no longer sufficient, leading to the popularity of fuzzy logic-based models. However, these models have limitations in dealing with complex, multi-criteria decision-making problems. To address this, a novel three-way group decision model is proposed in this paper, incorporating decision-theoretic rough sets and intuitionistic hesitant fuzzy sets. The model provides a more robust and accurate approach for selecting investment policies. Mathematical modeling confirms the effectiveness of the proposed model in comparison to existing methods.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Interdisciplinary Applications
Jin Ye, Jianming Zhan, Zeshui Xu
Summary: This paper proposes a novel decision-making method based on fuzzy rough sets to transform uncertain data into intuitionistic fuzzy data, establish a new MADM method, and introduce intuitionistic fuzzy weights and global intuitionistic fuzzy thresholds.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Runkang Li, Jilin Yang, Xianyong Zhang
Summary: In this paper, we propose a method to transform linguistic set-values into numerical values based on granular structures for multi-scale decision tables. We construct a relatively objective and comprehensive total cost for optimal scale selection, including test cost, delay cost, and misclassification cost. An OSS algorithm is designed based on the order of uncertainty and total cost. The feasibility and effectiveness of the algorithm are verified through experiments on UCI data sets.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Jie Yang, Xiaoqi Wang, Guoyin Wang, Deyou Xia
Summary: From the perspective of human cognition, three-way decision (3WD) explores thinking, problem solving, and information processing in three paradigms. Rough fuzzy sets (RFS) are constructed to handle fuzzy concepts by extending the classical rough sets. In three-way decision with rough fuzzy sets (3WDRFS), the introduction of uncertainty measure provides a new perspective for 3WD theory, and the proposed 3WDRFS with the idea of minimizing uncertainty loss demonstrates better performance than the 0.5-approximation model.
COGNITIVE COMPUTATION
(2023)
Article
Computer Science, Information Systems
Jiubing Liu, Jiaxin Mai, Huaxiong Li, Bing Huang, Yongjun Liu
Summary: The determination of both thresholds in decision-theoretic rough sets is an important problem that has attracted wide attention. This paper introduces linguistic intuitionistic fuzzy numbers (LIFNs) and a single optimization model-based method to determine the thresholds, achieving three-way decision. The proposed method overcomes the drawbacks of existing methods and has been validated through examples and experiments.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Kamal Kumar, Shyi-Ming Chen
Summary: This paper proposes an improved intuitionistic fuzzy Einstein weighted averaging operator to overcome the drawbacks of existing operators, and introduces a new multiattribute decision making method based on it.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Tahir Mahmood, Jabbar Ahmmad, Zeeshan Ali, Miin-Shen Yang
Summary: Due to the complexities of different diseases, accurate medical diagnosis has become a difficult task for experts. Researchers are developing new methods to overcome these difficulties. This article proposes novel techniques to aid experts in accurately diagnosing diseases, including the establishment of confidence-level intuitionistic fuzzy aggregation operators and a medical diagnosis model based on the intuitionistic fuzzy rough model.
Article
Computer Science, Information Systems
Decui Liang, Mingwei Wang, Zeshui Xu, Dun Liu
INFORMATION SCIENCES
(2020)
Article
Management
Decui Liang, Mingwei Wang, Zeshui Xu, Xu Chen
Summary: This study expands the classic Three-Way Decision (TWD) model by introducing the influence of regret psychology on decision-makers' risk behaviors and proposing a new risk Interval-Valued TWD model. Through simulation experiments, the impact of regret behavior on TWD is explored and the effectiveness of the new model is validated. The new model is applied to project resource allocation, improving decision flexibility under resource constraints.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2021)
Article
Computer Science, Artificial Intelligence
Mingwei Lin, Huibing Wang, Zeshui Xu
ARTIFICIAL INTELLIGENCE REVIEW
(2020)
Article
Computer Science, Artificial Intelligence
Huchang Liao, Cheng Zhang, Li Luo, Zeshui Xu, Jian-Bo Yang, Dong-Ling Xu
Article
Computer Science, Artificial Intelligence
Dun Liu, Xiaoqing Ye
KNOWLEDGE-BASED SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Dejian Yu, Zeshui Xu, Xizhao Wang
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2020)
Article
Computer Science, Artificial Intelligence
Xindi Wang, Xunjie Gou, Zeshui Xu
APPLIED SOFT COMPUTING
(2020)
Article
Computer Science, Artificial Intelligence
Cheng Zhang, Huchang Liao, Li Luo, Zeshui Xu
APPLIED SOFT COMPUTING
(2020)
Article
Automation & Control Systems
Yang Li, Yixin Zhang, Zeshui Xu
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Chaoqun Li, Hua Zhao, Zeshui Xu
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2020)
Article
Construction & Building Technology
Mingwei Lin, Chao Huang, Zeshui Xu
SUSTAINABLE CITIES AND SOCIETY
(2020)
Article
Computer Science, Information Systems
Mingwei Wang, Decui Liang, Zeshui Xu
INFORMATION SCIENCES
(2020)
Article
Management
Bo Li, Yixin Zhang, Zeshui Xu
Summary: The concept of limited interval-valued probabilistic linguistic term sets (-IVPLTSs) is proposed to address the information loss issue in the normalization process of PLTS. Basic operation laws and aggregation operators for -IVPLTSs are provided, and the membership degree is determined by the deviation degree through a programming model. Application of the method to airline service quality evaluation is discussed to verify its rationality.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2021)
Article
Management
Xiang Deng, Xiang Cheng, Jing Gu, Zeshui Xu
Summary: Empirical research shows that companies pursuing sustainable development have higher credit ratings and lower equity costs. However, there is no consensus on sustainable development indicators or investment decision-making methods. This study introduces a set of indicators and an optimization-based consensus model to guide investors in assessing sustainable development enterprises, contributing a new approach to multi-attribute group decision-making problems in the literature.
GROUP DECISION AND NEGOTIATION
(2021)
Article
Operations Research & Management Science
Bo Li, Yi-Xin Zhang, Ze-Shui Xu
JOURNAL OF THE OPERATIONS RESEARCH SOCIETY OF CHINA
(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)
