Supervised redundant feature detection for tumor classification

Abstract

Background: As a high dimensional problem, analysis of microarray data sets is a challenging task, where many weakly relevant or redundant features affect overall performance of classifiers. Methods: 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. This study propose a novel algorithm named RESI (Redundant fEature Selection depending on Instance), which considers label information in the measure of feature subset redundancy. Results: Experimental results on benchmark data sets show that RESI performs better than the previous state-of-the-art algorithms on redundant feature selection methods like mRMR. Conclusions: We propose an effective supervised redundant feature detection method for tumor classification.