2023
DOI: 10.3390/math11163452
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Unsupervised Attribute Reduction Algorithm for Mixed Data Based on Fuzzy Optimal Approximation Set

Abstract: Fuzzy rough set theory has been successfully applied to many attribute reduction methods, in which the lower approximation set plays a pivotal role. However, the definition of lower approximation used has ignored the information conveyed by the upper approximation and the boundary region. This oversight has resulted in an unreasonable relation representation of the target set. Despite the fact that scholars have proposed numerous enhancements to rough set models, such as the variable precision model, none have… Show more

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Cited by 4 publications
(1 citation statement)
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“…In the literature [27], Devi proposed a new dimension reduce technology by considering the picture of fuzzy soft matrices in the decision-making process. Wen [28] et al raised an unsupervised attribute reduction algorithm for mixed data based on fuzzy optimal approximation set. These above-mentioned classic reduction models are suitable only for complete systems.…”
Section: Introductionmentioning
confidence: 99%
“…In the literature [27], Devi proposed a new dimension reduce technology by considering the picture of fuzzy soft matrices in the decision-making process. Wen [28] et al raised an unsupervised attribute reduction algorithm for mixed data based on fuzzy optimal approximation set. These above-mentioned classic reduction models are suitable only for complete systems.…”
Section: Introductionmentioning
confidence: 99%