An ensemble method based on rotation calibrated least squares support vector machine for multi-source data classification

ABSTRACT This paper proposed an extended rotation-based ensemble method for the classification of a multi-source optical-radar data. The proposed method was actually inspired by the rotation-based support vector machine ensemble (RoSVM) with several fundamental refinements. In the first modification, a least squares support vector machine was used rather than the support vector machine due to its higher speed. The second modification was to apply a Platt calibrated version instead of a classical non-probabilistic version in order to consider more suitable probabilities for the classes. In the third modification, a filter-based feature selection algorithm was used rather than a wrapper algorithm in order to further speed up the proposed method. In the final modification, instead of a classical majority voting, an objective majority voting, which has better performance and less ambiguity, was employed for fusing the single classifiers. Accordingly, the proposed method was entitled rotation calibrated least squares support vector machine (RoCLSSVM). Then, it was compared to other SVM-based versions and also the RoSVM. The results implied higher accuracy, efficiency and diversity of the RoCLSSVM than the RoSVM for the classification of the data set of this paper. Furthermore, the RoCLSSVM had lower sensitivity to the training size than the RoSVM.