An improved D-S evidence theory based neighborhood rough classification approach

Article type: Research Article

Authors: Yin, Tao^{a; 1} | Mao, Xiaojuan^{b; 1} | Wu, Xingtan^a | Ju, Hengrong^{a; *} | Ding, Weiping^{a; *} | Yang, Xibei^c

Affiliations: [a] School of Information Science and Technology, Nantong University, Nantong, China | [b] Department of Respiratory Medicine, The Sixth People’s Hospital of Nantong/Affiliated Nantong Hospital of Shanghai University, Nantong, China | [c] School of Computer, Jiangsu University of Science and Technology, Zhenjiang, China

Correspondence: [*] Corresponding author. Hengrong Ju, E-mail: [email protected].; and Weiping Ding, E-mail: [email protected].

Note: [1] The first two authors made equivalent contributions and should be considered co-first author.

Abstract: Neighborhood classifier, a common classification method, is applied in pattern recognition and data mining. The neighborhood classifier mainly relies on the majority voting strategy to judge each category. This strategy only considers the number of samples in the neighborhood but ignores the distribution of samples, which leads to a decreased classification accuracy. To overcome the shortcomings and improve the classification performance, D-S evidence theory is applied to represent the evidence information support of other samples in the neighborhood, and the distance between samples in the neighborhood is taken into account. In this paper, a novel attribute reduction method of neighborhood rough set with a dynamic updating strategy is developed. Different from the traditional heuristic algorithm, the termination threshold of the proposed reduction algorithm is dynamically optimized. Therefore, when the attribute significance is not monotonic, this method can retrieve a better value, in contrast to the traditional method. Moreover, a new classification approach based on D-S evidence theory is proposed. Compared with the classical neighborhood classifier, this method considers the distribution of samples in the neighborhood, and evidence theory is applied to describe the closeness between samples. Finally, datasets from the UCI database are used to indicate that the improved reduction can achieve a lower neighborhood decision error rate than classical heuristic reduction. In addition, the improved classifier acquires higher classification performance in contrast to the traditional neighborhood classifier. This research provides a new direction for improving the accuracy of neighborhood classification.

Keywords: Attribute reduction, D-S evidence theory, neighborhood classification, rough set

DOI: 10.3233/JIFS-210462

Journal: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6601-6613, 2021