document: Redundancy based feature selection for microarray data In gene expression microarray data analysis , selecting a small number of discriminative genes from thousands of genes is an important problem for accurate classification of diseases or phenotypes . The problem becomes particularly challenging due to the large number of features ( genes ) and small sample size . Traditional gene selection methods often select the top-ranked genes according to their individual discriminative power without handling the high degree of redundancy among the genes . Latest research shows that removing redundant genes among selected ones can achieve a better representation of the characteristics of the targeted phenotypes and lead to improved classification accuracy . Hence , we study in this paper the relationship between feature relevance and redundancy and propose an efficient method that can effectively remove redundant genes . The efficiency and effectiveness of our method in comparison with representative methods has been demonstrated through an empirical study using public microarray data sets .
doc_bio_tags: O O O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O