{"title":"基于粗糙集理论的文本特征排序","authors":"Songbo Tan, Yuefen Wang, Xueqi Cheng","doi":"10.1109/WI.2007.150","DOIUrl":null,"url":null,"abstract":"With the aim to reduce the dimensionality without sacrificing classification performance, the author gains insights from attribute reduction based on discernibility matrix in rough-set theory and proposes two text feature selection algorithms, i.e., DB1 and DB2. The experimental results indicate that DB2 not only yields much higher accuracy than information gain when the number of features is smaller than 6000, but also incurs much smaller CPU time than information gain.","PeriodicalId":192501,"journal":{"name":"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Text Feature Ranking Based on Rough-set Theory\",\"authors\":\"Songbo Tan, Yuefen Wang, Xueqi Cheng\",\"doi\":\"10.1109/WI.2007.150\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the aim to reduce the dimensionality without sacrificing classification performance, the author gains insights from attribute reduction based on discernibility matrix in rough-set theory and proposes two text feature selection algorithms, i.e., DB1 and DB2. The experimental results indicate that DB2 not only yields much higher accuracy than information gain when the number of features is smaller than 6000, but also incurs much smaller CPU time than information gain.\",\"PeriodicalId\":192501,\"journal\":{\"name\":\"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WI.2007.150\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WI.2007.150","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
With the aim to reduce the dimensionality without sacrificing classification performance, the author gains insights from attribute reduction based on discernibility matrix in rough-set theory and proposes two text feature selection algorithms, i.e., DB1 and DB2. The experimental results indicate that DB2 not only yields much higher accuracy than information gain when the number of features is smaller than 6000, but also incurs much smaller CPU time than information gain.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
