{"title":"基于实例的冗余特征检测的监督解决方案","authors":"Xue-Qiang Zeng, Guozheng Li","doi":"10.1109/BIBMW.2012.6470320","DOIUrl":null,"url":null,"abstract":"As a high dimensional problem, analysis of microarray data sets is a challenging task, where many weakly relevant or redundant features hurt generalization performance of classifiers. The previous works used redundant feature detection methods to select discriminative compact gene set, which only considered the relationship among features, not the redundancy of classification ability among features. Here, we propose a novel algorithm named RESI (Redundant fEature Selection depending on Instance), which considers label information in the measure of feature subset redundancy. Experimental results on benchmark data sets show that RESI performs better than the previous state-of-arts algorithms on redundant feature selection methods like mRMR.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A supervised solution for redundant feature detection depending on instances\",\"authors\":\"Xue-Qiang Zeng, Guozheng Li\",\"doi\":\"10.1109/BIBMW.2012.6470320\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As a high dimensional problem, analysis of microarray data sets is a challenging task, where many weakly relevant or redundant features hurt generalization performance of classifiers. The previous works used redundant feature detection methods to select discriminative compact gene set, which only considered the relationship among features, not the redundancy of classification ability among features. Here, we propose a novel algorithm named RESI (Redundant fEature Selection depending on Instance), which considers label information in the measure of feature subset redundancy. Experimental results on benchmark data sets show that RESI performs better than the previous state-of-arts algorithms on redundant feature selection methods like mRMR.\",\"PeriodicalId\":6392,\"journal\":{\"name\":\"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIBMW.2012.6470320\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBMW.2012.6470320","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A supervised solution for redundant feature detection depending on instances
As a high dimensional problem, analysis of microarray data sets is a challenging task, where many weakly relevant or redundant features hurt generalization performance of classifiers. The previous works used redundant feature detection methods to select discriminative compact gene set, which only considered the relationship among features, not the redundancy of classification ability among features. Here, we propose a novel algorithm named RESI (Redundant fEature Selection depending on Instance), which considers label information in the measure of feature subset redundancy. Experimental results on benchmark data sets show that RESI performs better than the previous state-of-arts algorithms on redundant feature selection methods like mRMR.