Unsupervised elimination of redundant features using genetic programming

Abstract

While most feature selection algorithms focus on finding relevant features, few take the redundancy issue into account. We propose a nonlinear redundancy measure which uses genetic programming to find the redundancy quotient of a feature with respect to a subset of features. The proposed measure is unsupervised and works with unlabeled data. We introduce a forward selection algorithm which can be used along with the proposed measure to perform feature selection over the output of a feature ranking algorithm. The effectiveness of the proposed method is assessed by applying it to the output of the Chi-square (Χ2) feature ranker on a classification task. The results show significant improvements in the performance of decision tree and SVM classifiers. © Springer-Verlag Berlin Heidelberg 2009.