A Novel Hybrid Feature Selection Algorithm for Hierarchical Classification

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, to the best of our knowledge, few studies in the literature address feature selection for the 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 the proteins and images domains to perform computational experiments to validate the effect of the proposed algorithm on classification performance when using two global hierarchical classifiers proposed in the literature. Statistical tests showed that using our method for feature selection led to predictive performances that were consistently better than 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.