Finding feature transformation functions using genetic algorithm

Identifying a good set of features is critical to the performance of learning algorithms such as classifiers. Previous methods have focused on either selecting a subset of features or transforming features using principle components analysis. In this paper, we propose a genetic algorithm approach that searches for a good feature transformation function over a subset of features using a novel representation scheme with novel reproduction operators. Preliminary experimental results using the UCI data set show promising results.