Related references
Note: Only part of the references are listed.
Article
Computer Science, Artificial Intelligence
Jianming Zhan et al.
Summary: This article introduces the combination of three-way decision theory and regret theory, proposes a novel method for estimating incomplete utility values, and establishes a wide sense three-way decision model for incomplete multiscale decision information systems. The feasibility, validity, and stability of the model are verified through experiments and parametric analyses.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Review
Computer Science, Artificial Intelligence
Xin Yang et al.
Summary: The concept of three-way decision, focusing on thinking, problem solving, and information processing in threes, has been extensively studied and applied in the fields of machine learning and data engineering in recent years. The integration of dynamic and uncertainty through multi-granularity learning in an open-world environment has brought new vitality to three-way decision. This paper investigates and summarizes the initial and development models of three-way decision, traces the historical line of sequential three-way decision from rough set to granular computing, and proposes a unified framework of three-way multi-granularity learning with four crucial problems on mining uncertain regions continuously. Additionally, proposals on three-way decision associated with open-continual learning are provided.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2023)
Article
Automation & Control Systems
Jianming Zhan et al.
Summary: This paper reviews and examines the advances in three-way behavioral decision making with hesitant fuzzy information systems from the perspective of the past, present, and future. It provides a brief historical account and presents basic formulations, summarizes the latest development trends and examines basic issues, and points out key challenges and future research directions.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2023)
Article
Computer Science, Information Systems
Tianxing Wang et al.
Summary: This paper combines prospect theory with regret theory to study the compound risk preference modeling of three-way decision and applies it to conflict analysis. The results show that the proposed three-way decision model based on compounded risk preference can effectively solve software development conflict analysis problem.
INFORMATION SCIENCES
(2023)
Article
Automation & Control Systems
Jiubing Liu et al.
Summary: Due to the effectiveness and advantages of interval-valued intuitionistic fuzzy sets (IVIFSs) in evaluating uncertainty and risk, we introduce IVIFSs into loss functions of decision-theoretic rough sets (DTRSs) and propose an optimization-based approach to interval-valued intuitionistic fuzzy three-way decisions. First, based on the classical DTRSs and two previous optimization models, we construct a new concise linear programming model for simultaneously determining the threshold pair. Second, we extend the constructed model via the IVIFSs of loss functions and discuss the relations between these loss functions based on ranking methods. Third, we develop our extended models via two ranking methods and prove the existence and uniqueness of the optimal solution of the model. The advantages of our approach compared to existing methods are demonstrated through an illustrative example.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Xianfeng Huang et al.
Summary: This paper introduces a new multivariate fusion prediction system that addresses the issues of mining prediction information from multi-scale information systems, describing the irrational behavior of decision-makers in prediction processes, and improving the prediction performance and generalization capability of the models. The system establishes the score function of each feature under multi-scale information systems using scale rules, regret theory, and dominance relations, and develops an adaptive S3WD model for feature selection. It also develops an improved version of the kernel extreme learning machine (KELM) called IKELM to enhance its effectiveness and rationality. The experimental results demonstrate that the proposed system outperforms existing machine learning models in terms of prediction performance and generalization ability.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Ge Hao et al.
Summary: The study introduces a new method for evaluating uncertainty, combining the granularity of knowledge, the cardinality of approximation sets, and the granularity distribution of the sets. The proposed measurements are shown to effectively evaluate uncertainty in information systems and decision systems through theoretical analyses and experiments on UCI data sets.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Artificial Intelligence
Jiucheng Xu et al.
Summary: Feature selection based on the fuzzy neighborhood rough set model (FNRS) is popular in data mining, but it may lead to the loss of information due to the dependency function only considering the lower approximation of the decision. This paper proposes a fuzzy neighborhood joint entropy model (FNSIJE) to address this problem, introducing uncertain fuzzy neighborhood self-information measures of decision variables and an uncertainty measure based on fuzzy neighborhood joint entropy for feature selection. The model shows better classification performance and can reduce dimensionality effectively.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Liangdong Qu et al.
Summary: This paper proposes a novel entropy measure for a fuzzy relation and applies it to attribute reduction in heterogeneous data. The proposed measure overcomes the weakness of existing measures and shows good feasibility and efficiency in experiments.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Xianyong Zhang et al.
Summary: This paper introduces the concept of tri-level attribute reduction, including classification-specific, class-specific, and object-specific reducts. By defining object-specific reducts and analyzing hierarchical relationships between different levels of reducts, the research enriches the study of attribute reduction.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Xin Yang et al.
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
Computer Science, Information Systems
Hongyuan Gou et al.
Summary: This paper improves uncertainty measurement and feature selection through fuzzy neighborhood rough sets (FNRSs) by proposing two measurement strategies and three measure-based heuristic feature selection algorithms to enhance classification performance.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Weiping Ding et al.
Summary: Research on efficient attribute reduction for massive dynamic datasets is important. Traditional incremental methods are inefficient when applied to large datasets. This study proposes an incremental acceleration strategy based on attribute trees, clustering attributes into multiple trees to improve efficiency, and introducing a branch coefficient in the stop criterion to avoid redundant calculations.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Jiefang Jiang et al.
Summary: Feature selection plays a crucial role in machine learning and knowledge acquisition. This study proposes a divergence-based fuzzy rough sets method and introduces double-quantitative feature selection algorithms to improve the existing algorithms and achieve better classification performance.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Jin Qian et al.
Summary: This study discusses the importance of hierarchical classification in machine learning and proposes a generalized hierarchical multigranulation sequential three-way decision model for multi-level and multi-view data. Experimental results demonstrate its suitability for various applications.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Wenbin Qian et al.
Summary: Multi-label learning deals with instances corresponding to multiple labels, learning the mapping from instance to label set. Label distribution learning improves results by considering different label significances.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2021)
Article
Computer Science, Information Systems
Can Gao et al.
Summary: This paper introduces a semi-supervised attribute reduction method based on rough sets, which utilizes information granularity and entropy measure to optimize attribute reduction for partially labeled data by generating proxy labels and using a fast algorithm.
INFORMATION SCIENCES
(2021)
Article
Physics, Multidisciplinary
Jiucheng Xu et al.
Summary: This paper proposes a feature selection algorithm that combines information theory view and algebraic view in the neighborhood decision system to improve classification performance. Experimental results demonstrate that the algorithm can effectively select optimal feature subsets on multiple datasets.
Article
Computer Science, Artificial Intelligence
Binbin Sang et al.
Summary: Based on the fuzzy dominance neighborhood rough set, this study proposes incremental feature selection approaches for dynamic interval-valued ordered data, and experimentally verifies the robustness of the proposed metric and the effectiveness of the incremental algorithms.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Hong Zhao et al.
Summary: In recent years, datasets have dramatically increased in size, leading to a rise in hierarchical classification tasks. Traditional feature selection algorithms often overlook the semantic relationships in hierarchical categories, while the proposed framework in this paper takes hierarchy into account. Experimental results have demonstrated the effectiveness and efficiency of the algorithm.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Yiyu Yao
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2020)
Article
Computer Science, Artificial Intelligence
Yiyu Yao
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2020)
Article
Computer Science, Artificial Intelligence
Lin Sun et al.
KNOWLEDGE-BASED SYSTEMS
(2020)
Article
Computer Science, Information Systems
Xi-Ao Ma
INFORMATION SCIENCES
(2020)
Article
Computer Science, Information Systems
Qinghua Zhang et al.
INFORMATION SCIENCES
(2020)
Article
Computer Science, Artificial Intelligence
Saul Solorio-Fernandez et al.
PATTERN RECOGNITION LETTERS
(2020)
Article
Computer Science, Artificial Intelligence
Changzhong Wang et al.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2019)
Article
Computer Science, Information Systems
Lin Sun et al.
INFORMATION SCIENCES
(2019)
Article
Computer Science, Information Systems
Ningxin Xie et al.
INFORMATION SCIENCES
(2019)
Article
Computer Science, Artificial Intelligence
Can Gao et al.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2019)
Article
Computer Science, Artificial Intelligence
Chengxiang Hu et al.
KNOWLEDGE-BASED SYSTEMS
(2019)
Article
Computer Science, Artificial Intelligence
Xiaodong Fan et al.
KNOWLEDGE-BASED SYSTEMS
(2018)
Article
Computer Science, Interdisciplinary Applications
Yumin Chen et al.
JOURNAL OF BIOMEDICAL INFORMATICS
(2017)
Article
Computer Science, Artificial Intelligence
Yumin Chen et al.
KNOWLEDGE-BASED SYSTEMS
(2017)
Article
Computer Science, Artificial Intelligence
Guoyin Wang et al.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2015)
Article
Computer Science, Artificial Intelligence
Feng Jiang et al.
PATTERN RECOGNITION
(2015)
Article
Computer Science, Information Systems
Yumin Chen et al.
INFORMATION SCIENCES
(2014)
Article
Computer Science, Information Systems
D. Q. Miao et al.
INFORMATION SCIENCES
(2009)
Article
Computer Science, Artificial Intelligence
Jerzy Blaszczynski et al.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2009)
Article
Computer Science, Artificial Intelligence
Qinghua Hu et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2008)
