{"title":"粗糙集中基于可辨矩阵的属性约简算法及混合决策树的生成","authors":"Yang Shuqing, Li Bo","doi":"10.1109/ISISE.2010.153","DOIUrl":null,"url":null,"abstract":"Through the use of the Discernibility Matrix in Rough Set, this paper introduced an Attribute Reduction Algorithm, based on which, a new one is put forward about the Generation of Hybrid Decision Tree. This Algorithm improved the traditional method as the attributes with high frequency of occurrence in the Discernibility Matrix can classify more examples at a time. Finally, by the comparison between the Algorithm and ID3, the new Algorithm is proved to be more superior and advantageous.","PeriodicalId":206833,"journal":{"name":"2010 Third International Symposium on Information Science and Engineering","volume":"176 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Attribute Reduction Algorithm and the Generation of Hybrid Decision Tree Based on Discernibility Matrix in Rough Set\",\"authors\":\"Yang Shuqing, Li Bo\",\"doi\":\"10.1109/ISISE.2010.153\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Through the use of the Discernibility Matrix in Rough Set, this paper introduced an Attribute Reduction Algorithm, based on which, a new one is put forward about the Generation of Hybrid Decision Tree. This Algorithm improved the traditional method as the attributes with high frequency of occurrence in the Discernibility Matrix can classify more examples at a time. Finally, by the comparison between the Algorithm and ID3, the new Algorithm is proved to be more superior and advantageous.\",\"PeriodicalId\":206833,\"journal\":{\"name\":\"2010 Third International Symposium on Information Science and Engineering\",\"volume\":\"176 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Third International Symposium on Information Science and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISISE.2010.153\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Third International Symposium on Information Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISISE.2010.153","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Attribute Reduction Algorithm and the Generation of Hybrid Decision Tree Based on Discernibility Matrix in Rough Set
Through the use of the Discernibility Matrix in Rough Set, this paper introduced an Attribute Reduction Algorithm, based on which, a new one is put forward about the Generation of Hybrid Decision Tree. This Algorithm improved the traditional method as the attributes with high frequency of occurrence in the Discernibility Matrix can classify more examples at a time. Finally, by the comparison between the Algorithm and ID3, the new Algorithm is proved to be more superior and advantageous.
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