JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2021, Vol. 56 ›› Issue (3): 77-82.doi: 10.6040/j.issn.1671-9352.4.2020.281

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Local rough set model based on similarity relation

ZHANG Jie, ZHANG Yan-lan*   

  1. School of Computer Science, Key Laboratory of Data Science and Intelligence Application, Fujian Province University, Fujian Province Key Laboratory of Granular Computing and Its Application, Minnan Normal University, Zhangzhou 363000, Fujian, China
  • Published:2021-03-16

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Abstract: Rough set theory is a supervised learning model, which generally requires a certain amount of labeled data to train classifiers. However, there are many unlabeled data in some practical problems, and the cost of labeling data is too large. The concept approximation is a key problem in rough set theory. The rough set based on similarity relation expands the application of rough set theory. In order to deal with the problem of limited label characteristics and low calculation efficiency, a theoretical model of local rough set under similarity relationship is introduced and a concept approximation model with linear time complexity is proposed. Theoretical proof and case analysis verify the superiority of local rough set based on similarity relation.

Key words: local rough set, similarity relation, concept approximation, limited labeled data

CLC Number: 

  • TP18
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