{"title":"基于互信息改进和遗传算法的特征选择方法","authors":"Y. Qiu, Peiyu Liu, Yuzhen Yang","doi":"10.1109/ITIME.2009.5236305","DOIUrl":null,"url":null,"abstract":"The feature selection is a key method of text categorization technology, this paper proposed a text feature selection method based on the improved of mutual information and genetic algorithm. Used the improved of mutual information algorithm to do the initial choose to removing redundancy and noise words at first, and then used the genetic algorithm to training the template which generate by a subset of words, so get the optimal feature subset that on behalf of the issue space, to achieve dimensionality reduction and improved classification accuracy.","PeriodicalId":398477,"journal":{"name":"2009 IEEE International Symposium on IT in Medicine & Education","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Feature selection method based on the improved of mutual information and genetic algorithm\",\"authors\":\"Y. Qiu, Peiyu Liu, Yuzhen Yang\",\"doi\":\"10.1109/ITIME.2009.5236305\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The feature selection is a key method of text categorization technology, this paper proposed a text feature selection method based on the improved of mutual information and genetic algorithm. Used the improved of mutual information algorithm to do the initial choose to removing redundancy and noise words at first, and then used the genetic algorithm to training the template which generate by a subset of words, so get the optimal feature subset that on behalf of the issue space, to achieve dimensionality reduction and improved classification accuracy.\",\"PeriodicalId\":398477,\"journal\":{\"name\":\"2009 IEEE International Symposium on IT in Medicine & Education\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE International Symposium on IT in Medicine & Education\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITIME.2009.5236305\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Symposium on IT in Medicine & Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITIME.2009.5236305","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Feature selection method based on the improved of mutual information and genetic algorithm
The feature selection is a key method of text categorization technology, this paper proposed a text feature selection method based on the improved of mutual information and genetic algorithm. Used the improved of mutual information algorithm to do the initial choose to removing redundancy and noise words at first, and then used the genetic algorithm to training the template which generate by a subset of words, so get the optimal feature subset that on behalf of the issue space, to achieve dimensionality reduction and improved classification accuracy.