{"title":"微阵列基因表达数据的最小冗余特征选择","authors":"C. Ding, Hanchuan Peng","doi":"10.1109/CSB.2003.1227396","DOIUrl":null,"url":null,"abstract":"Selecting a small subset of genes out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their differential expressions among phenotypes and pick the top-ranked genes. We observe that feature sets so obtained have certain redundancy and study methods to minimize it. Feature sets obtained through the minimum redundancy - maximum relevance framework represent broader spectrum of characteristics of phenotypes than those obtained through standard ranking methods; they are more robust, generalize well to unseen data, and lead to significantly improved classifications in extensive experiments on 5 gene expressions data sets.","PeriodicalId":147883,"journal":{"name":"Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2467","resultStr":"{\"title\":\"Minimum redundancy feature selection from microarray gene expression data\",\"authors\":\"C. Ding, Hanchuan Peng\",\"doi\":\"10.1109/CSB.2003.1227396\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Selecting a small subset of genes out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their differential expressions among phenotypes and pick the top-ranked genes. We observe that feature sets so obtained have certain redundancy and study methods to minimize it. Feature sets obtained through the minimum redundancy - maximum relevance framework represent broader spectrum of characteristics of phenotypes than those obtained through standard ranking methods; they are more robust, generalize well to unseen data, and lead to significantly improved classifications in extensive experiments on 5 gene expressions data sets.\",\"PeriodicalId\":147883,\"journal\":{\"name\":\"Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-08-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2467\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSB.2003.1227396\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSB.2003.1227396","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Minimum redundancy feature selection from microarray gene expression data
Selecting a small subset of genes out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their differential expressions among phenotypes and pick the top-ranked genes. We observe that feature sets so obtained have certain redundancy and study methods to minimize it. Feature sets obtained through the minimum redundancy - maximum relevance framework represent broader spectrum of characteristics of phenotypes than those obtained through standard ranking methods; they are more robust, generalize well to unseen data, and lead to significantly improved classifications in extensive experiments on 5 gene expressions data sets.