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A Comparative Study on Feature Selection Methods for Drug Discovery
Journal of chemical information and computer sciences
Feature selection is frequently used as a preprocessing step to machine learning. The removal of irrelevant and redundant information often improves the performance of learning algorithms. This paper is a comparative study of feature selection in drug discovery. The focus is on aggressive dimensionality reduction. Five methods were evaluated, including information gain, mutual information, a 2 -test, odds ratio, and GSS coefficient. Two well-known classification algorithms, Naïve Bayesian anddoi:10.1021/ci049875d pmid:15446842 fatcat:elaceijbdnbbhmwlf24n4s2dd4