A classification method by integrating density and neighborhood covering reduction

ZHANG Qinghua; AI Zhihua; ZHANG Jinzhen

doi:10.15983/j.cnki.jsnu.2022105

Journal of Shaanxi Normal University(Natural Science Edition) >

2022 , Vol. 50 >Issue 3: 33 - 42

DOI: https://doi.org/10.15983/j.cnki.jsnu.2022105

A classification method by integrating density and neighborhood covering reduction

ZHANG Qinghua ,
AI Zhihua ,
ZHANG Jinzhen

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Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received date: 2021-04-03

Online published: 2022-05-10

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Abstract

For the problem that most existing classification methods based on neighborhood covering directly classify samples according to the nearest neighborhood. The certain classification methods do not consider the differences between each neighborhood, which may lead to classification mistakes. Local density is introduced to characterize the differences between each neighborhood, and a classification method by integrating density and neighborhood covering reduction(D-NCR) is proposed in this paper. First, the redundant neighborhoods are eliminated by the neighborhood covering reduction. Then, the weighted distance from the test sample to the neighborhood center is defined through local density of neighborhood center. Finally, two classification strategies are proposed for different test samples based on the weighted distance. Experimental results on UCI data sets demonstrate the effectiveness of the proposed method.

Key words： neighborhood rough set; neighborhood covering reduction; local density; weighted distance; classification strategy

Cite this article

ZHANG Qinghua , AI Zhihua , ZHANG Jinzhen . A classification method by integrating density and neighborhood covering reduction[J]. Journal of Shaanxi Normal University(Natural Science Edition), 2022 , 50(3) : 33 -42 . DOI: 10.15983/j.cnki.jsnu.2022105

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