In order to boost the diversity among individual classifiers of an ensemble, a new ensemble method is proposed that combines two different classifier models via a transformation of rotation forest, named by isomerous multiple classifier ensemble．Firstly, the original samples are transformed and divided by the rotating forest to obtain new samples．Then support vector machine with the high accuracy of classification or kernel matching pursuit with the speedy classification is selected as a basic classifier model based on a special proportion, the selected classifier is used to classify the new samples, and the predictive labels are obtained. Finally, the predictive labels given by two different models are combined to obtain the final predictive labels of an ensemble. Particularly, the proposed method achieves the complementarity of accuracy and speed by combining two different classifier models, and it is important that isomerous classifier ensemble improve the generalization error of an ensemble and increases the classification performance. According to the experimental results of classification for UCI datasets and remote sensing image datasets, it is illustrated that the proposed method shortens obviously the running time and improves the accuracy of classification, compared with an ensemble based on the single classifier model.
