Exploring the effectiveness of dynamic ensemble selection in the one-versus-one scheme

Abstract

The One-versus-One (OVO) strategy is one of the most common and effective techniques to deal with multi-class classification problems. The basic idea of an OVO scheme is to divide a multi-class classification problem into several easier-to-solve binary classification problems with considering each possible pair of classes from the original problem, which is then built into a binary classifier by an independent base learner. In this study, we propose a novel methodology which attempts to select a group of base classifiers in each pairwise dataset for each unknown pattern. To implement this, the Dynamic Ensemble Selection (DES) method based on a competence measure is employed to select the most appropriate ensemble in each binary classification problem derived from the OVO decomposition. In order to verify the validity and effectiveness of our proposed method, we carry out a thorough experimental study. We first compare our proposal with several state-of-the-art approaches. Then, we perform the comparison of several well-known aggregation strategies to combine the binary ensemble obtained by Dynamic Ensemble Selection. Finally, we explore whether further improvement can be achieved by considering the competence-based method in OVO scheme. The extracted findings drawn from the empirical analysis are supported by the proper statistical analysis and indicate that there is a positive synergy between the DES method and the Distance-based Relative Competence Weighting (DRCW) approach for the OVO scheme.