IJCAS

The selective ensemble aims to search the optimal subset balanced accuracy and diversity from the original base classifier set to construct an ensemble classifier with strong generalization performance. A selective ensemble classifier named BRFS-APCSC is proposed in this paper, which realizes the generation and selection of a set of accurate and diverse base classifiers respectively. In the first step, a multimodal perturbation method is introduced to train distinct base classifiers. The method perturbs the sample space by Bootstrap and disturbs the feature space under a newly proposed semi-random feature selection, which is a combination of the core attribute theory and the improved maximum relevance minimum redundancy algorithm. Then, to search the optimal classifier subset, affinity propagation clustering is added to cluster base classifiers in the first step, then the base classifiers are regarded as features so that the improved maximum relevance minimum redundancy algorithm is applied to select parts of base classifiers from each cluster for integration. UCI datasets and an actual dataset of semi-decarbonization are employed to verify the performance of BRFS-APCSC. The experimental results demonstrate that BRFS-APCSC has significantly difference with other selective ensemble methods and improve the classification accuracy.

Keywords: Affinity propagation clustering, maximum relevance minimum redundancy, multimodal perturbation method, selective ensemble.