{"title":"基于改进最小熵原理的特征选择","authors":"Jr-Shian Chen, Hung-Lieh Chou, D. Tai","doi":"10.1109/ICEIE.2010.5559828","DOIUrl":null,"url":null,"abstract":"Feature selections have seen growing importance placed on statistics, pattern recognition, machine learning and data mining. Researchers have demonstrated the interest in the methods for improving the performance of their forecasting results. Therefore, this study proposes a feature selection approach, which based on minimize entropy principle approach. Experimental results have shown that the proposed model provided more average accuracy rate and stability then other methods.","PeriodicalId":211301,"journal":{"name":"2010 International Conference on Electronics and Information Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Feature selection based on modified minimize entropy principle\",\"authors\":\"Jr-Shian Chen, Hung-Lieh Chou, D. Tai\",\"doi\":\"10.1109/ICEIE.2010.5559828\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Feature selections have seen growing importance placed on statistics, pattern recognition, machine learning and data mining. Researchers have demonstrated the interest in the methods for improving the performance of their forecasting results. Therefore, this study proposes a feature selection approach, which based on minimize entropy principle approach. Experimental results have shown that the proposed model provided more average accuracy rate and stability then other methods.\",\"PeriodicalId\":211301,\"journal\":{\"name\":\"2010 International Conference on Electronics and Information Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Electronics and Information Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEIE.2010.5559828\",\"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 International Conference on Electronics and Information Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEIE.2010.5559828","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Feature selection based on modified minimize entropy principle
Feature selections have seen growing importance placed on statistics, pattern recognition, machine learning and data mining. Researchers have demonstrated the interest in the methods for improving the performance of their forecasting results. Therefore, this study proposes a feature selection approach, which based on minimize entropy principle approach. Experimental results have shown that the proposed model provided more average accuracy rate and stability then other methods.