Exploring Attribute Selection in Hierarchical Classification

Abstract

In the domain of many classification problems, classes have relations of dependency that are represented in hierarchical structures. These problems are known as hierarchical classification problems. Methods based on different approaches, considering hierarchical relations in different ways, have been proposed to solve them, in the attempt to achieve better predictive performance. In this work, we explore attribute selection techniques in conjunction with hierarchical classifiers from different categories, with the goal of improving their respective performances. Computational experiments, made with 18 hierarchical datasets, have indicated that the adopted classifiers attain better predictive accuracy when the most relevant attributes are considered in their construction.