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
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, Information Systems
Dandan Zou, Yaoliang Xu, Lingqiang Li, Zhenming Ma
Summary: Variable precision (fuzzy) rough sets are generalizations of Pawlak rough sets that handle uncertain and imprecise information well. However, many existing variable precision (fuzzy) rough sets lack the comparable property (CP), which is fundamental in Pawlak rough sets. To address this issue, a novel variable precision fuzzy rough set model with CP is proposed, along with an associated three-way decision model.
INFORMATION SCIENCES
(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
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
Chengyong Jin, Bao Qing Hu
Summary: This paper introduces the concept of hesitant sets to unify different types of fuzzy sets. It discusses the construction of decision evaluation functions and three-way decisions based on hesitant sets in three-way decision spaces. The paper presents methods for constructing decision evaluation functions in semi-three-way and quasi-three-way decision spaces, as well as transformation methods to three-way decision spaces. The importance of these methods is supported by numerous examples.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoxue Wang, Xianyong Zhang
Summary: Rough sets and vague sets are fundamental methods for uncertainty, and their integration through rough vague sets provides a robust platform for data analysis. To improve vague sets, we propose linear-combined rough vague sets and model them as probabilistic rough sets using three-way decision, while also investigating optimization of uncertainty measurement.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Information Systems
Xiaonan Li, Xuan Wang, Bingzhen Sun, Yanhong She, Lu Zhao
Summary: This paper generalizes the model of three-way decision from 0-1 tables to general information tables, with the assignment of values to the set of objects and the construction of tri-partitions. The fundamental result identifies finitely many pairs of thresholds and describes the variation of the positive region based on thresholds. The evaluation of these finite tri-partitions by weighted entropy allows for obtaining an optimal tri-partition.
INFORMATION SCIENCES
(2021)
Editorial Material
Computer Science, Artificial Intelligence
JingTao Yao, Jesus Medina, Yan Zhang, Dominik Slezak
Summary: Formal concept analysis, rough sets, and three-way decisions are prominent theories and methods for data representation and analysis, widely applied to data mining, machine learning, artificial intelligence, etc.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2022)
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
Ning Wang, Ping Zhu
Summary: The three-way decision model based on linguistic term sets has been extensively studied in order to incorporate natural language evaluations into decision-making processes. This paper introduces a three-way decision model based on computing with words, which translates linguistic information into linguistic distribution assessments for better analysis. The model is further enhanced by incorporating decision-theoretic rough fuzzy sets using computing with words. A fabricated example demonstrates the model's ability to handle general linguistic information.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
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
Jaroslaw Stepaniuk, Andrzej Skowron
Summary: We discuss a three-way rough set based approach for approximating decision granules in Intelligent Systems (IS's). The approach introduces a new concept of approximation space based on advanced reasoning tools. Many generalizations of rough set approaches in the past have focused on reasoning involving (partial) set inclusion, but such approximation spaces are not sufficient for dealing with important aspects of approximate reasoning in IS's. The paper demonstrates this claim through various examples and emphasizes the involvement of complex algorithmic optimization processes driven by reasoning tools in solving the considered problems.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2023)
Article
Computer Science, Artificial Intelligence
Jianming Zhan, Haibo Jiang, Yiyu Yao
Summary: This article introduces three-way decisions into multiattribute decision-making based on an outranking relation, presents three strategies to design a new 3WD model, and demonstrates the rationality and effectiveness of the proposed method through solving practical problems and experimental evaluations. The comparative analysis shows that the proposed 3WD method is effective and practically useful.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Information Systems
Wenyan Xu, Yucong Yan, Xiaonan Li
Summary: This paper introduces two models of three-way decision with ranking and reference tuple on hybrid information tables. The models are assessed using a unique measure, and concepts of local optimal and global optimal trisections are proposed. Through comparison and experiments, the models demonstrate strong expressive power and feasibility in potential applications.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Xiaonan Li, Huangjian Yi, Sanyang Liu
INFORMATION SCIENCES
(2016)
Article
Computer Science, Theory & Methods
Xiaonan Li, Huangjian Yi
FUZZY SETS AND SYSTEMS
(2017)
Article
Computer Science, Artificial Intelligence
Xiaonan Li, Bingzhen Sun, Yanhong She
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2017)
Article
Computer Science, Artificial Intelligence
Xiaonan Li, Huangjian Yi
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2017)
Article
Computer Science, Interdisciplinary Applications
Bingzhen Sun, Weimin Ma, Xiangtang Chen, Xiaonan Li
COMPUTERS & INDUSTRIAL ENGINEERING
(2018)
Article
Computer Science, Artificial Intelligence
Xiaonan Li, Huangjian Yi, Zhaohao Wang
Article
Computer Science, Artificial Intelligence
Xiaonan Li
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2019)
Article
Computer Science, Information Systems
Xiaonan Li, Qianqian Sun, Hongmei Chen, Huangjian Yi
INFORMATION SCIENCES
(2020)
Article
Computer Science, Artificial Intelligence
Xiaonan Li, Xuan Wang, Guangming Lang, Huangjian Yi
Summary: This paper studies three-way conflict analysis based on TFISs, proposes the concept of TFISs and establishes a TFIS for conflict analysis, defines relative area Delta S to describe concrete attitudes of agents, and attaches fuzzy weights to issues according to agents' TFSJMs.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2021)
Article
Computer Science, Information Systems
Xiaonan Li, Xuan Wang, Bingzhen Sun, Yanhong She, Lu Zhao
Summary: This paper generalizes the model of three-way decision from 0-1 tables to general information tables, with the assignment of values to the set of objects and the construction of tri-partitions. The fundamental result identifies finitely many pairs of thresholds and describes the variation of the positive region based on thresholds. The evaluation of these finite tri-partitions by weighted entropy allows for obtaining an optimal tri-partition.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Huangjian Yi, Huimin Zhang, Xiaonan Li, Yanpo Yang
Summary: This paper explores conflict analysis in a hesitant fuzzy setting, proposing a model based on hesitant fuzzy information systems. It introduces the concepts of conflict, neutrality, and alliance sets on one issue and multiple issues, defines the degrees of conflict, neutrality, and alliance among agents, and discusses two methods of computing coalitions based on Bayesian decision theory and finding complete subgraphs of the coalition graph.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2021)
Article
Computer Science, Artificial Intelligence
Xiaonan Li, Yanpo Yang, Huangjian Yi, Qianqian Yu
Summary: This paper delves into a three-way conflict analysis decision model, incorporating trapezoidal fuzzy numbers, threshold calculation, attitude integration, among other key elements, to offer a new perspective and approach to resolving conflict situations.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Bingzhen Sun, Weimin Ma, Xiaonan Li
APPLIED SOFT COMPUTING
(2017)
Article
Mathematics, Applied
X. Li, H. Yi
IRANIAN JOURNAL OF FUZZY SYSTEMS
(2017)
Article
Computer Science, Artificial Intelligence
Timotheus Kampik, Kristijonas Cyras, Jose Ruiz Alarcon
Summary: This paper presents a formal approach to explaining changes in inference in Quantitative Bipolar Argumentation Frameworks (QBAFs). The approach traces the causes of strength inconsistencies and provides explanations for them.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Xiangnan Zhou, Longchun Wang, Qingguo Li
Summary: This paper aims to establish a closer connection between domain theory and Formal Concept Analysis (FCA) by introducing the concept of an optimized concept for a formal context. With the utilization of optimized concepts, it is demonstrated that the class of formal contexts directly corresponds to algebraic domains. Additionally, two subclasses of formal contexts are identified to characterize algebraic L-domains and Scott domains. An application is presented to address the open problem of reconstructing bounded complete continuous domains using attribute continuous contexts, and the presentation of algebraic domains is extended to a categorical equivalence.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Sihan Wang, Zhong Yuan, Chuan Luo, Hongmei Chen, Dezhong Peng
Summary: Anomaly detection is widely used in various fields, but most current methods only work for specific data and ignore uncertain information such as fuzziness. This paper proposes an anomaly detection algorithm based on fuzzy rough entropy, which effectively addresses the similarity between high-dimensional objects using distance and correlation measures. The algorithm is compared and analyzed with mainstream anomaly detection algorithms on publicly available datasets, showing superior performance and flexibility.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Mario Alviano, Francesco Bartoli, Marco Botta, Roberto Esposito, Laura Giordano, Daniele Theseider Dupre
Summary: This paper investigates the relationships between a multipreferential semantics in defeasible reasoning and a multilayer neural network model. Weighted knowledge bases are considered for a simple description logic with typicality under a concept-wise multipreference semantics. The semantics is used to interpret MultiLayer Perceptrons (MLPs) preferentially. Model checking and entailment based approach are employed in verifying conditional properties of MLPs.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Bazin Alexandre, Galasso Jessie, Kahn Giacomo
Summary: Formal concept analysis is a mathematical framework that represents the information in binary object-attribute datasets using a lattice of formal concepts. It has been extended to handle more complex data types, such as relational data and n-ary relations. This paper presents a framework for polyadic relational concept analysis, which extends relational concept analysis to handle relational datasets consisting of n-ary relations.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Ander Gray, Marcelo Forets, Christian Schilling, Scott Ferson, Luis Benet
Summary: The presented method combines reachability analysis and probability bounds analysis to handle imprecisely known random variables. It can rigorously compute the temporal evolution of p-boxes and provide interval probabilities for formal verification problems. The method does not impose strict constraints on the input probability distribution or p-box and can handle multivariate p-boxes with a consonant approximation method.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Laszlo Csato
Summary: This paper studies a special type of incomplete pairwise comparison matrices and proposes a new method to determine the missing elements without violating the ordinal property.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
