Selected tree classifier combination based on both accuracy and error diversity

This paper proposes a method for combining multiple tree classifiers based on both classifier ensemble (bagging) and dynamic classifier selection schemes (DCS). The proposed method is composed of the following procedures: (1) building individual tree classifiers based on bootstrap samples; (2) calculating the distance between all possible two trees; (3) clustering the trees based on single linkage clustering; (4) selecting two clusters by local region in terms of accuracy and error diversity; and (5) voting the results of tree classifiers selected in the two clusters. Empirical evaluation using publicly available data sets confirms the superiority of our proposed approach over other classifier combining methods.