An improved D-S evidence theory based neighborhood rough classification approach
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 distrib...
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Veröffentlicht in: | Journal of intelligent & fuzzy systems 2021-01, Vol.41 (6), p.6601-6613 |
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creator | Yin, Tao Mao, Xiaojuan Wu, Xingtan Ju, Hengrong Ding, Weiping Yang, Xibei |
description | 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. |
doi_str_mv | 10.3233/JIFS-210462 |
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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. 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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.</description><subject>Algorithms</subject><subject>Classification</subject><subject>Classifiers</subject><subject>Data mining</subject><subject>Heuristic methods</subject><subject>Neighborhoods</subject><subject>Pattern recognition</subject><subject>Reduction</subject><subject>Rough set models</subject><issn>1064-1246</issn><issn>1875-8967</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNotkE9LAzEQxYMoWKsnv0DAo0Tzb7ObY6lWKwWF6jlkk2w3pd3UZLfQb2_KOpcZmMf7zTwA7gl-YpSx54_lYo0owVzQCzAhVVmgSoryMs9YcEQoF9fgJqUtxqQsKJ6Ar1kH_f4Qw9FZ-ILW0B29dZ1xsG9diCdY65Q3nfObtg6xDcHCGIZNC81Op-Qbb3TvQwf1IZto096Cq0bvkrv771Pws3j9nr-j1efbcj5bIUMF6ZGUFbeNsYzwileWsXwQzdU0sqS1ZdLpQorCClqWtcCY1bqoJHVYivweJ2wKHkbfjP0dXOrVNgyxy0iVAVhygWWRVY-jysSQUnSNOkS_1_GkCFbnyNQ5MjVGxv4A3CBcwQ</recordid><startdate>20210101</startdate><enddate>20210101</enddate><creator>Yin, Tao</creator><creator>Mao, Xiaojuan</creator><creator>Wu, Xingtan</creator><creator>Ju, Hengrong</creator><creator>Ding, Weiping</creator><creator>Yang, Xibei</creator><general>IOS Press BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20210101</creationdate><title>An improved D-S evidence theory based neighborhood rough classification approach</title><author>Yin, Tao ; Mao, Xiaojuan ; Wu, Xingtan ; Ju, Hengrong ; Ding, Weiping ; Yang, Xibei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c261t-9984dfcd314848d330172222ff972bd39ea5965d6277b6003ba5892e096104413</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Classification</topic><topic>Classifiers</topic><topic>Data mining</topic><topic>Heuristic methods</topic><topic>Neighborhoods</topic><topic>Pattern recognition</topic><topic>Reduction</topic><topic>Rough set models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yin, Tao</creatorcontrib><creatorcontrib>Mao, Xiaojuan</creatorcontrib><creatorcontrib>Wu, Xingtan</creatorcontrib><creatorcontrib>Ju, Hengrong</creatorcontrib><creatorcontrib>Ding, Weiping</creatorcontrib><creatorcontrib>Yang, Xibei</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Journal of intelligent & fuzzy systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yin, Tao</au><au>Mao, Xiaojuan</au><au>Wu, Xingtan</au><au>Ju, Hengrong</au><au>Ding, Weiping</au><au>Yang, Xibei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An improved D-S evidence theory based neighborhood rough classification approach</atitle><jtitle>Journal of intelligent & fuzzy systems</jtitle><date>2021-01-01</date><risdate>2021</risdate><volume>41</volume><issue>6</issue><spage>6601</spage><epage>6613</epage><pages>6601-6613</pages><issn>1064-1246</issn><eissn>1875-8967</eissn><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.</abstract><cop>Amsterdam</cop><pub>IOS Press BV</pub><doi>10.3233/JIFS-210462</doi><tpages>13</tpages></addata></record> |
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subjects | Algorithms Classification Classifiers Data mining Heuristic methods Neighborhoods Pattern recognition Reduction Rough set models |
title | An improved D-S evidence theory based neighborhood rough classification approach |
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