Multilabel Feature Selection Based on Relative Discernibility Pair Matrix

Yao, Erliang; Li, Deyu; Zhai, Yongjie; Zhang, Chao

doi:10.1109/tfuzz.2021.3082171

Cited by 23 publications

(1 citation statement)

References 55 publications

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“…The decision rules are obtained by reducing attributes and objects in Pawlak's rough sets. Hence, there are many attribute and object reduction methods, such as the discernibility matrix method [30,31], positive region method [32,33], information entropy method [34,35] and other methods [36,37]. Different reduction methods correspond to different rules, which will influence the decision result.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Criteria Decision-Making Method Based on Rough Sets and Fuzzy Measures

Wang

Zhang

2022

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Rough set theory provides a useful tool for data analysis, data mining and decision making. For multi-criteria decision making (MCDM), rough sets are used to obtain decision rules by reducing attributes and objects. However, different reduction methods correspond to different rules, which will influence the decision result. To solve this problem, we propose a novel method for MCDM based on rough sets and a fuzzy measure in this paper. Firstly, a type of non-additive measure of attributes is presented by the importance degree in rough sets, which is a fuzzy measure and called an attribute measure. Secondly, for a decision information system, the notion of the matching degree between two objects is presented under an attribute. Thirdly, based on the notions of the attribute measure and matching degree, a Choquet integral is constructed. Moreover, a novel MCDM method is presented by the Choquet integral. Finally, the presented method is compared with other methods through a numerical example, which is used to illustrate the feasibility and effectiveness of our method.

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