{"title":"模糊等价关系下基于粗糙集的属性约简方法研究","authors":"Guorui Jiang, G. Zang","doi":"10.1109/CIS.2007.151","DOIUrl":null,"url":null,"abstract":"In this paper, first we proposed a method of attribute reduction based on rough set under fuzzy equivalent relation. We computed the similarity of cases with the fuzzy equivalent relation, reduced the attributes by the same fuzzy equivalent partitions based on rough set, and then gave a method of computing the weights of the attributes. Comparing with the traditional method of attribute reduction based on rough set, more information of the primary data is held, and more accuracy of the attribute reduction is enhanced by our method.","PeriodicalId":127238,"journal":{"name":"2007 International Conference on Computational Intelligence and Security (CIS 2007)","volume":"87 14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Study on the Method of Attribute Reduction Based on Rough Set under Fuzzy Equivalent Relation\",\"authors\":\"Guorui Jiang, G. Zang\",\"doi\":\"10.1109/CIS.2007.151\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, first we proposed a method of attribute reduction based on rough set under fuzzy equivalent relation. We computed the similarity of cases with the fuzzy equivalent relation, reduced the attributes by the same fuzzy equivalent partitions based on rough set, and then gave a method of computing the weights of the attributes. Comparing with the traditional method of attribute reduction based on rough set, more information of the primary data is held, and more accuracy of the attribute reduction is enhanced by our method.\",\"PeriodicalId\":127238,\"journal\":{\"name\":\"2007 International Conference on Computational Intelligence and Security (CIS 2007)\",\"volume\":\"87 14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-12-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 International Conference on Computational Intelligence and Security (CIS 2007)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIS.2007.151\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 International Conference on Computational Intelligence and Security (CIS 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIS.2007.151","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Study on the Method of Attribute Reduction Based on Rough Set under Fuzzy Equivalent Relation
In this paper, first we proposed a method of attribute reduction based on rough set under fuzzy equivalent relation. We computed the similarity of cases with the fuzzy equivalent relation, reduced the attributes by the same fuzzy equivalent partitions based on rough set, and then gave a method of computing the weights of the attributes. Comparing with the traditional method of attribute reduction based on rough set, more information of the primary data is held, and more accuracy of the attribute reduction is enhanced by our method.