Effective heterogeneous ensemble classification: An alternative approach for selecting base classifiers

Abstract In this paper, an alternative approach to select base classifiers forming a parallel Heterogeneous ensemble is proposed. The fundamental concept is to trim poorly performing classifiers; thus, a more effective Heterogeneous ensemble can be generated. More specifically, the proposed trimming approach finds an optimal subset of classifiers to form the desired heterogeneous ensemble. The main challenge is how to detect poor performance classifiers. To address this issue, the differences in effectiveness between base classifiers forming the ensemble are utilized to spot weak classifiers. For evaluating the proposed approach, eighteen benchmark datasets are used for generating the heterogeneous ensemble classification and comparisons with the state-of-the-art methods are conducted. The experimental analysis demonstrated the effectiveness and superiority of the proposed approach when compared to other state-of-the-art approaches.