Minimum redundancy feature selection from microarray gene expression data

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.

Keywords

Redundancy (engineering)Minimum redundancy feature selectionFeature selectionMicroarray analysis techniquesComputer scienceData miningGenePhenotypePattern recognition (psychology)Ranking (information retrieval)Artificial intelligenceFeature (linguistics)Computational biologyData redundancyBiologyGeneticsGene expression

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Publication Info

Year: 2004
Type: article
Pages: 523-528
Citations: 496
Access: Closed

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APA Style

                            
                                    C. Ding, 
                                
                                    Hujin Peng
                                
                            (2004). 
                            Minimum redundancy feature selection from microarray gene expression data. 
                            
                            , 523-528.
                            https://doi.org/10.1109/csb.2003.1227396

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DOI: 10.1109/csb.2003.1227396