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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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Kong X, Zhou M, Bian K, Lai W, Hu F, Dai R, Yan J. Research on SPDTRS-PNN based intelligent assistant diagnosis for breast cancer. Sci Rep 2023; 13:4386. [PMID: 36928059 PMCID: PMC10020448 DOI: 10.1038/s41598-023-28316-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 01/17/2023] [Indexed: 03/18/2023] Open
Abstract
Breast cancer is the second dangerous cancer in the world. Breast cancer data often contains more redundant information. Redundant information makes the breast cancer auxiliary diagnosis less accurate and time consuming. Dimension reduction algorithm combined with machine learning can solve these problems well. This paper proposes the single parameter decision theoretic rough set (SPDTRS) combined with the probability neural network (PNN) model for breast cancer diagnosis. We find that when the parameter value of SPDTRS is 2.5 and the SPREAD value is 0.75, the number of 30 attributes of the original breast cancer data dropped to 12, the accuracy of the SPDTRS-PNN model training set is 99.25%, the accuracy of the test set is 97.04%, and the test time is 0.093 s. The experimental results show that the SPDTRS-PNN model can improve the ac-curacy of breast cancer recognition, reduce the time required for diagnosis.
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Affiliation(s)
- Xixi Kong
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, China.
| | - Mengran Zhou
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, China
| | - Kai Bian
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, China
| | - Wenhao Lai
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, China
| | - Feng Hu
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, China
| | - Rongying Dai
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, China
| | - Jingjing Yan
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, China
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A novel approach to multi-attribute predictive analysis based on rough fuzzy sets. APPL INTELL 2023. [DOI: 10.1007/s10489-022-04360-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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4
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The movement strategy of three-way decisions based on clustering. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.01.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Jia Z, Qiao J. New constructions of decision evaluation functions in three-way decision spaces based on uninorms. Artif Intell Rev 2022; 56:5881-5927. [PMID: 36407012 PMCID: PMC9660152 DOI: 10.1007/s10462-022-10316-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In 2014, Hu introduced the concept of three-way decision spaces and axiomatic definition of decision evaluation functions. In three-way decision spaces, decision evaluation function satisfies minimum element axiom, monotonicity axiom and complement axiom. Since then, the research on construction method of decision evaluation functions from commonly used binary aggregation functions becomes a research hotspot. Meanwhile, uninorms, as one class of binary aggregation functions, have been successfully applied in various application problems, such as in decision making, image processing, data mining, etc. This paper continues to consider this research topic and mainly explores the new construction methods of decision evaluation functions based on uninorms. Firstly, we show two novel transformation methods from semi-decision evaluation functions to decision evaluation functions based on uninorms. Secondly, using known semi-decision evaluation functions, we give some new construction methods of semi-decision evaluation functions. Thirdly, we give some novel construction methods of decision evaluation functions and semi-decision evaluation functions related to fuzzy sets, interval-valued fuzzy sets, fuzzy relations and hesitant fuzzy sets. Based on them, decision maker can obtain more useful decision evaluation functions, thereby more choices can be used for realistic decision-making problems. Finally, we consider two real evaluation problems to illustrate the results obtained in this paper. The three-way decisions results of evaluation problem show that the construction method proposed in this paper is superior to some existing construction methods under some conditions.
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Affiliation(s)
- Zihang Jia
- College of Mathematics and Statistics, Northwest Normal University, No. 967, Anning East Road, Lanzhou, 730070 Gansu People’s Republic of China
- School of Mathematical Sciences, Dalian University of Technology, No. 2, Linggong Road, Dalian, 116024 Liaoning People’s Republic of China
| | - Junsheng Qiao
- College of Mathematics and Statistics, Northwest Normal University, No. 967, Anning East Road, Lanzhou, 730070 Gansu People’s Republic of China
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Ye J, Sun B, Zhan J, Chu X. Variable precision multi-granulation composite rough sets with multi-decision and their applications to medical diagnosis. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.10.037] [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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Zhang D, Zhu P. Variable radius neighborhood rough sets and attribute reduction. Int J Approx Reason 2022. [DOI: 10.1016/j.ijar.2022.08.005] [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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Fan J, Wang P, Jiang C, Yang X, Song J. Ensemble learning using three-way density-sensitive spectral clustering. Int J Approx Reason 2022. [DOI: 10.1016/j.ijar.2022.07.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Attribute reduction algorithm of neighborhood rough set based on supervised granulation and its application. Soft comput 2022. [DOI: 10.1007/s00500-022-07454-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Attribute Reduction Based on Lift and Random Sampling. Symmetry (Basel) 2022. [DOI: 10.3390/sym14091828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
As one of the key topics in the development of neighborhood rough set, attribute reduction has attracted extensive attentions because of its practicability and interpretability for dimension reduction or feature selection. Although the random sampling strategy has been introduced in attribute reduction to avoid overfitting, uncontrollable sampling may still affect the efficiency of search reduct. By utilizing inherent characteristics of each label, Multi-label learning with Label specIfic FeaTures (Lift) algorithm can improve the performance of mathematical modeling. Therefore, here, it is attempted to use Lift algorithm to guide the sampling for reduce the uncontrollability of sampling. In this paper, an attribute reduction algorithm based on Lift and random sampling called ARLRS is proposed, which aims to improve the efficiency of searching reduct. Firstly, Lift algorithm is used to choose the samples from the dataset as the members of the first group, then the reduct of the first group is calculated. Secondly, random sampling strategy is used to divide the rest of samples into groups which have symmetry structure. Finally, the reducts are calculated group-by-group, which is guided by the maintenance of the reducts’ classification performance. Comparing with other 5 attribute reduction strategies based on rough set theory over 17 University of California Irvine (UCI) datasets, experimental results show that: (1) ARLRS algorithm can significantly reduce the time consumption of searching reduct; (2) the reduct derived from ARLRS algorithm can provide satisfying performance in classification tasks.
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Statistical-mean double-quantitative K-nearest neighbor classification learning based on neighborhood distance measurement. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Abstract
The complexity of the data type and distribution leads to the increase in uncertainty in the relationship between samples, which brings challenges to effectively mining the potential cluster structure of data. Ensemble clustering aims to obtain a unified cluster division by fusing multiple different base clustering results. This paper proposes a three-way ensemble clustering algorithm based on sample’s perturbation theory to solve the problem of inaccurate decision making caused by inaccurate information or insufficient data. The algorithm first combines the natural nearest neighbor algorithm to generate two sets of perturbed data sets, randomly extracts the feature subsets of the samples, and uses the traditional clustering algorithm to obtain different base clusters. The sample’s stability is obtained by using the co-association matrix and determinacy function, and then the samples can be divided into a stable region and unstable region according to a threshold for the sample’s stability. The stable region consists of high-stability samples and is divided into the core region of each cluster using the K-means algorithm. The unstable region consists of low-stability samples and is assigned to the fringe regions of each cluster. Therefore, a three-way clustering result is formed. The experimental results show that the proposed algorithm in this paper can obtain better clustering results compared with other clustering ensemble algorithms on the UCI Machine Learning Repository data set, and can effectively reveal the clustering structure.
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Yang HL, Xue SY, She YH. General three-way decision models on incomplete information tables. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.05.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Abstract
As a powerful data analysis technique, clustering plays an important role in data mining. Traditional hard clustering uses one set with a crisp boundary to represent a cluster, which cannot solve the problem of inaccurate decision-making caused by inaccurate information or insufficient data. In order to solve this problem, three-way clustering was presented to show the uncertainty information in the dataset by adding the concept of fringe region. In this paper, we present an improved three-way clustering algorithm based on an ensemble strategy. Different to the existing clustering ensemble methods by using various clustering algorithms to produce the base clustering results, the proposed algorithm randomly extracts a feature subset of samples and uses the traditional clustering algorithm to obtain the diverse base clustering results. Based on the base clustering results, labels matching is used to align all clustering results in a given order and voting method is used to obtain the core region and the fringe region of the three way clustering. The proposed algorithm can be applied on the top of any existing hard clustering algorithm to generate the base clustering results. As examples for demonstration, we apply the proposed algorithm on the top of K-means and spectral clustering, respectively. The experimental results show that the proposed algorithm is effective in revealing cluster structures.
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Zhu J, Ma X, Zhan J. A regret theory-based three-way decision approach with three strategies. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.02.030] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Ge H, Yang C, Xu Y. Incremental updating three-way regions with variations of objects and attributes in incomplete neighborhood systems. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.10.046] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Bai J, Sun B, Chu X, Wang T, Li H, Huang Q. Neighborhood rough set-based multi-attribute prediction approach and its application of gout patients. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2021.108127] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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22
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Yuan K, Xu W, Li W, Ding W. An incremental learning mechanism for object classification based on progressive fuzzy three-way concept. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.10.058] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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23
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Qian L. Research on complex attribute big data classification based on iterative fuzzy clustering algorithm. WEB INTELLIGENCE 2021. [DOI: 10.3233/web-210463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In order to overcome the low classification accuracy of traditional methods, this paper proposes a new classification method of complex attribute big data based on iterative fuzzy clustering algorithm. Firstly, principal component analysis and kernel local Fisher discriminant analysis were used to reduce dimensionality of complex attribute big data. Then, the Bloom Filter data structure is introduced to eliminate the redundancy of the complex attribute big data after dimensionality reduction. Secondly, the redundant complex attribute big data is classified in parallel by iterative fuzzy clustering algorithm, so as to complete the complex attribute big data classification. Finally, the simulation results show that the accuracy, the normalized mutual information index and the Richter’s index of the proposed method are close to 1, the classification accuracy is high, and the RDV value is low, which indicates that the proposed method has high classification effectiveness and fast convergence speed.
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Affiliation(s)
- Li Qian
- School of Digital Information Technology, Zhejiang Technical Institute of Economics, Hangzhou 310018, China. E-mail:
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Wang T, Sun B, Jiang C, Weng H, Chu X. Kernel alignment-based three-way clustering on attribute space and its application in stroke risk identification. INT J MACH LEARN CYB 2021. [DOI: 10.1007/s13042-021-01478-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Wu C, Zhang Q, Cheng Y, Gao M, Wang G. Novel three-way generative classifier with weighted scoring distribution. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.08.025] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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27
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Weak-label-based global and local multi-view multi-label learning with three-way clustering. INT J MACH LEARN CYB 2021. [DOI: 10.1007/s13042-021-01450-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Yang X, Chen H, Li T, Wan J, Sang B. Neighborhood rough sets with distance metric learning for feature selection. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107076] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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