GA-KDE-Bayes: an evolutionary wrapper method based on non-parametric density estimation applied to bioinformatics problems

This paper presents an evolutionary wrapper method for feature selection that uses a non-parametric density estimation method and a Bayesian Classifier. Non-parametric methods are a good alternative for scarce and sparse data, as in Bioinformatics problems, since they do not make any assumptions about its structure and all the information come from data itself. Results show that local modeling provides small and relevant subsets of features when comparing to results available on literature.