Evaluating the Dynamicity of Feature and Individual Classifiers Selection in Ensembles of Classifiers

Abstract

A feature selection method has the objective of selecting the best feature subset that represents the entire dataset. The majority of these methods apply a static selection procedure, since it selects a feature subset and uses it throughout the classification. Recently, dynamic feature selection has been emerged as an efficient alternative for feature selection. Instead of selecting the feature subset for the entire dataset, a dynamic method selects the best feature subset for an individual instance or a group of instances and, in this sense, each instance or group will have its own feature subset. The use of feature selection methods helps to improve the accuracy in classification tasks, using either single classifiers or ensemble of classifiers. In the context of ensembles, a feature selection method selects the best feature subset for each individual classifier to be used in an ensemble. In this paper, we propose an investigation of integrating dynamic feature selection (DFS) in ensemble of classifiers. More specifically, the use of DFS methods in dynamic ensemble methods, in which an ensemble structure (individual classifiers) is selected for each testing instance. Our main objective is to promote dynamicity in ensembles of classifiers in order to obtain more robust ensembles. In order to accomplish this investigation, two well known dynamic ensemble methods are chosen to be analyzed. Our findings indicated real benefits when integrating the dynamic feature selection method with the dynamic ensemble selection methods, for the majority of cases.