A Novel Hybrid Feature Selection Algorithm for Hierarchical Classification
Helen C. S. C. Lima, Fernando E. B. Otero, Luiz Henrique de Campos Merschmann, Marcone Jamilson Freitas Souza
Abstract
Feature selection is a widespread preprocessing step in the data mining field. One of its purposes is to reduce the number of original dataset features to improve a predictive model’s performance. Despite the benefits of feature selection for the classification task, as far as we are aware, few studies in the literature address feature selection for hierarchical classification context. This paper proposes a novel feature selection method based on the General Variable Neighborhood Search metaheuristic, combining a filter and a wrapper step, wherein a global model hierarchical classifier evaluates feature subsets. We used twelve datasets from protein and image domains to perform computational experiments to validate the proposed algorithm effect on classification performance when using two global hierarchical classifiers proposed in the literature. Statistical tests showed that using our method as a feature selection led to a predictive performance that is consistently better or equivalent to that obtained by using all features, with the benefit of reducing the number of features needed, which justifies its efficiency for the hierarchical classification scenario.