Ensemble classifier optimization by reducing input features and base classifiers

Ensemble classifier approaches either exploit the input feature space also known as the dataset attributes, or exploit the data sample space, for example Random Forest (RaF) exploits input features whereas Bagging exploits data sample space. Very few ensemble classifier approaches exist that exploit the both. In this paper we propose an ensemble classifier approach that first reduces input feature space by selecting only the significant features from input data that can maximize the classification performance of the ensemble and then optimize the base classifier pool by incorporating an evolutionary algorithm. The proposed approach is evaluated on benchmark datasets from UCI repository. The results are compared with single classifier approaches and existing state of the art ensemble classifier approaches.