A VNS algorithm for feature selection in hierarchical classification context

Feature selection, usually adopted as a preprocessing step for data mining, is used to select a subset of predictive features aiming to improve the performance of a predictive model. Despite of the benefits of feature selection for classification task, to the best of our knowledge, there is no work in the literature that addresses feature selection in conjunction with global hierarchical classifiers. Thus, in this paper, we fill this gap proposing a feature selection method based on Variable Neighborhood Search (VNS) metaheuristic for the hierarchical classification context. Computational experiments were carried out on five bioinformatics datasets to evaluate the effect of the proposed algorithm on classification performance when using a global hierarchical classifier. As result, we have obtained a classifier performance improvement for three datasets and a competitive result for a fourth dataset, which indicates the suitability of the proposed method for the hierarchical classification scenario.