Generalized fuzzy variable precision rough sets based on bisimulations and the corresponding decision-making

Zhang, Li; Zhu, Ping

doi:10.1007/s13042-022-01527-5

“…Therefore, Chen et al [12,13] discussed the general granular structure of fuzzy rough sets and gave another granular structure expression of fuzzy rough sets. Due to the existence complexity and uncertainty of many practical problems, several extended fuzzy rough set models have been proposed to meet various requirements [14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Local fuzzy rough set model over two universes and its reduction

Xie

¹

,

Lin

²

,

Li

³

et al. 2023

Soft Comput

1

0

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The fuzzy information system over two universes formalizes a data table corresponding to two universes as well as their relations. Although many analyses have focused on this topic, the existing models are mixed with some uncertain factors in the process of given relation, and it is unnecessary to analyze the fuzzy objects with small membership degree. This motivates us to develop a better method to handle complex data. This paper proposes a local fuzzy rough set model over two universes, then further analyzes its properties and decision rules. Moreover, the reduction method of the model is being studied. The experiment results show that the proposed model has better performance in classfication and decision making.

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“…Rahman et al [26] proposed a MADM approach combined with AND OR operations, which can effectively deal with the uncertainties of possibility neutrosophic hypersoft set. Zhang et al [27] proposed a bisimulationbased generalized fuzzy variable precision rough set model to solve the problem of the decision-making, which can effectively tackle complicated problems including the attribute and the relational data. Ye et al [28] proposed a fuzzy rough sets-enabled decision-making approach, which can effectively tackle the uncertainty and imprecision problem in MADM.…”

Section: Introductionmentioning

confidence: 99%

A Rule-Based Decision Support Method Combining Variable Precision Rough Set and Stochastic Multi-Objective Acceptability Analysis for Multi-Attribute Decision-Making

Zhu

¹

,

Liu

²

,

Zhang

³

et al. 2022

Mathematical Problems in Engineering

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In an open environment, the demands of users are diverse and dynamic because users can participate in product design from beginning to end. Owing to this, the disorderly and unplanned participation of users will greatly increase the complexity of multi-attribute decision-making (MADM) in the product design process. In order to ensure the smooth development of the open design process, the decision support model and method need to repeatedly provide decision makers (DMs) with necessary decision support information in a relatively short period of time, which can realize the evaluation of the scheme and improve the utilization efficiency of community resources. However, the process of eliciting preference information is complex, exhausting, inefficient, and time-consuming in existing methods, which will result in a poor decision-making. With the purpose of optimizing the eliciting process in MADM, a rule-based decision support method is proposed in this paper, where the process of eliciting preference information and decision-making are synchronized and guided by pre-extracted decision rules. The rules are deduced from comparison relations on attributes and their outcomes through the combination of variable precision rough set approach (VPRS) and stochastic multi-objective acceptability analysis (SMAA). With the concept of attribute reduction and approximation accuracy in rough set theory, the extracted rules could eliminate redundant attributes and assign the relative priority of preference information. Based on the extracted rules, the multi-attribute decision-making process could be carried out step by step in an orderly manner. In each step, DMs only need to provide partial preference information by non-quantitative statements according to extracted rules. Once the decision result is reliable enough, the eliciting and decision-making process can be terminated promptly. In order to validate the proposed approach, experiments of decision rule extraction are implemented, and the results show that the proposed approach is effective both in the weak rule extraction and the strong rule extraction.

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“…Therefore, Chen et al [12,13] discussed the general granular structure of fuzzy rough sets and gave another granular structure expression of fuzzy rough sets. Due to the existence complexity and uncertainty of many practical problems, several extended fuzzy rough set models have been proposed to meet various requirements [14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Local fuzzy rough set model over two universes and its reduction

Xie

¹

,

Lin

²

,

Li

³

et al. 2022

Preprint

1

0

View full text Add to dashboard Cite

The fuzzy information system over two universes formalizes a data table corresponding to two universes as well as their relations. Although many analyses have focused on this topic, the existing models are mixed with some uncertain factors in the process of given relation, and it is unnecessary to analyze the fuzzy objects with small membership degree. This motivates us to develop a better method to handle complex data. This paper proposes a local fuzzy rough set model over two universes, then further analyzes its properties and decision rules. Moreover, the reduction method of the model is being studied. The experiment results show that the proposed model has better performance in classfication and decision making.

show abstract

Generalized fuzzy variable precision rough sets based on bisimulations and the corresponding decision-making

Cited by 8 publications

References 66 publications

Local fuzzy rough set model over two universes and its reduction

Local fuzzy rough set model over two universes and its reduction

A Rule-Based Decision Support Method Combining Variable Precision Rough Set and Stochastic Multi-Objective Acceptability Analysis for Multi-Attribute Decision-Making

Local fuzzy rough set model over two universes and its reduction

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