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Ding J, Zhang C, Li D, Zhan J, Li W, Yao Y. Three-way decisions in generalized intuitionistic fuzzy environments: survey and challenges. Artif Intell Rev 2024; 57:38. [PMID: 38333110 PMCID: PMC10847217 DOI: 10.1007/s10462-023-10647-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
Enhancing decision-making under risks is crucial in various fields, and three-way decision (3WD) methods have been extensively utilized and proven to be effective in numerous scenarios. However, traditional methods may not be sufficient when addressing intricate decision-making scenarios characterized by uncertain and ambiguous information. In response to this challenge, the generalized intuitionistic fuzzy set (IFS) theory extends the conventional fuzzy set theory by introducing two pivotal concepts, i.e., membership degrees and non-membership degrees. These concepts offer a more comprehensive means of portraying the relationship between elements and fuzzy concepts, thereby boosting the ability to model complex problems. The generalized IFS theory brings about heightened flexibility and precision in problem-solving, allowing for a more thorough and accurate description of intricate phenomena. Consequently, the generalized IFS theory emerges as a more refined tool for articulating fuzzy phenomena. The paper offers a thorough review of the research advancements made in 3WD methods within the context of generalized intuitionistic fuzzy (IF) environments. First, the paper summarizes fundamental aspects of 3WD methods and the IFS theory. Second, the paper discusses the latest development trends, including the application of these methods in new fields and the development of new hybrid methods. Furthermore, the paper analyzes the strengths and weaknesses of research methods employed in recent years. While these methods have yielded impressive outcomes in decision-making, there are still some limitations and challenges that need to be addressed. Finally, the paper proposes key challenges and future research directions. Overall, the paper offers a comprehensive and insightful review of the latest research progress on 3WD methods in generalized IF environments, which can provide guidance for scholars and engineers in the intelligent decision-making field with situations characterized by various uncertainties.
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Affiliation(s)
- Juanjuan Ding
- School of Computer and Information Technology, Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, 030006 Shanxi China
| | - Chao Zhang
- School of Computer and Information Technology, Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, 030006 Shanxi China
| | - Deyu Li
- School of Computer and Information Technology, Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, 030006 Shanxi China
| | - Jianming Zhan
- School of Mathematics and Statistics, Hubei Minzu University, Enshi, 445000 Hubei China
| | - Wentao Li
- School of Computer and Information Technology, Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, 030006 Shanxi China
- College of Artificial Intelligence, Southwest University, Chongqing, 400715 China
| | - Yiyu Yao
- Department of Computer Science, University of Regina, Regina, Saskatchewan S4S 0A2 Canada
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Yin Y. Prediction and analysis of time series data based on granular computing. Front Comput Neurosci 2023; 17:1192876. [PMID: 37576071 PMCID: PMC10413556 DOI: 10.3389/fncom.2023.1192876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 07/06/2023] [Indexed: 08/15/2023] Open
Abstract
The advent of the Big Data era and the rapid development of the Internet of Things have led to a dramatic increase in the amount of data from various time series. How to classify, correlation rule mining and prediction of these large-sample time series data has a crucial role. However, due to the characteristics of high dimensionality, large data volume and transmission lag of sensor data, large sample time series data are affected by multiple factors and have complex characteristics such as multi-scale, non-linearity and burstiness. Traditional time series prediction methods are no longer applicable to the study of large sample time series data. Granular computing has unique advantages in dealing with continuous and complex data, and can compensate for the limitations of traditional support vector machines in dealing with large sample data. Therefore, this paper proposes to combine granular computing theory with support vector machines to achieve large-sample time series data prediction. Firstly, the definition of time series is analyzed, and the basic principles of traditional time series forecasting methods and granular computing are investigated. Secondly, in terms of predicting the trend of data changes, it is proposed to apply the fuzzy granulation algorithm to first convert the sample data into coarser granules. Then, it is combined with a support vector machine to predict the range of change of continuous time series data over a period of time. The results of the simulation experiments show that the proposed model is able to make accurate predictions of the range of data changes in future time periods. Compared with other prediction models, the proposed model reduces the complexity of the samples and improves the prediction accuracy.
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Affiliation(s)
- Yushan Yin
- School of Electro-Mechanical Engineering, Xidian University, Xi’an, China
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Zhang X, Shang J, Wang J. Multi-granulation fuzzy rough sets based on overlap functions with a new approach to MAGDM. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2022.11.146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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A review of sequential three-way decision and multi-granularity learning. Int J Approx Reason 2022. [DOI: 10.1016/j.ijar.2022.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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Liu K, Li T, Yang X, Yang X, Liu D. Neighborhood rough set based ensemble feature selection with cross-class sample granulation. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Zhang X, Jiang Z, Xu W. Feature selection using a weighted method in interval-valued decision information systems. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03987-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Neighborhood based concept lattice. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03828-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Qian W, Zhou Y, Qian J, Wang Y. Cost-sensitive sequential three-way decision for information system with fuzzy decision. Int J Approx Reason 2022. [DOI: 10.1016/j.ijar.2022.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Feature selection using self-information uncertainty measures in neighborhood information systems. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03760-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Multi-granularity dominance rough concept attribute reduction over hybrid information systems and its application in clinical decision-making. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.03.048] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Incremental neighborhood entropy-based feature selection for mixed-type data under the variation of feature set. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02526-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Han Z, Huang Q, Zhang J, Huang C, Wang H, Huang X. GA-GWNN: Detecting anomalies of online learners by granular computing and graph wavelet convolutional neural network. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03337-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Xu J, Yang J, Ma Y, Qu K, Kang Y. Feature selection method for color image steganalysis based on fuzzy neighborhood conditional entropy. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02923-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Xu W, Yuan K, Li W. Dynamic updating approximations of local generalized multigranulation neighborhood rough set. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02861-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Multi-attribute group three-way decision making with degree-based linguistic term sets. Int J Approx Reason 2021. [DOI: 10.1016/j.ijar.2021.07.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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A development framework of granular prototypes with an allocation of information granularity. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.06.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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A novel approach of two-stage three-way co-opetition decision for crowdsourcing task allocation scheme. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.01.048] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Incremental fuzzy probability decision-theoretic approaches to dynamic three-way approximations. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2020.10.043] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Sang B, Chen H, Yang L, Zhou D, Li T, Xu W. Incremental attribute reduction approaches for ordered data with time-evolving objects. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106583] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Yang X, Zhang Y, Fujita H, Liu D, Li T. Local temporal-spatial multi-granularity learning for sequential three-way granular computing. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.06.020] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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Partial-overall dominance three-way decision models in interval-valued decision systems. Int J Approx Reason 2020. [DOI: 10.1016/j.ijar.2020.08.014] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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