A Neighborhood Rough Set Attribute Reduction Method Based on Attribute Importance

Su, Peiyu and Qin, Feng and Li, Fu (2023) A Neighborhood Rough Set Attribute Reduction Method Based on Attribute Importance. American Journal of Computational Mathematics, 13 (04). pp. 578-593. ISSN 2161-1203

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Abstract

Attribute reduction is a hot topic in rough set research. As an extension of rough sets, neighborhood rough sets can effectively solve the problem of information loss after data discretization. However, traditional greedy-based neighborhood rough set attribute reduction algorithms have a high computational complexity and long processing time. In this paper, a novel attribute reduction algorithm based on attribute importance is proposed. By using conditional information, the attribute reduction problem in neighborhood rough sets is discussed, and the importance of attributes is measured by conditional information gain. The algorithm iteratively removes the attribute with the lowest importance, thus achieving the goal of attribute reduction. Six groups of UCI datasets are selected, and the proposed algorithm SAR is compared with L2-ELM, LapTELM, CTSVM, and TBSVM classifiers. The results demonstrate that SAR can effectively improve the time consumption and accuracy issues in attribute reduction.

Item Type: Article
Subjects: Science Global Plos > Mathematical Science
Depositing User: Unnamed user with email support@science.globalplos.com
Date Deposited: 14 Dec 2023 06:53
Last Modified: 14 Dec 2023 06:53
URI: http://ebooks.manu2sent.com/id/eprint/2380

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