Algorithm of ensemble of multi-cassifier based on roughness

The ensemble classifiers train their base classifiers through certain datasets which are generated by some rules. This paper presents algorithm, called MDR, based on membership degree and roughness of rough set to divide the original datasets into two parts. One part is easy to classify while another is hard. Two different base classifiers are trained for fitting them; those two kinds of classifiers are integrated as base classifiers. This method is applied to classify the UCI benchmark datasets, and the experimental results show that this method is superior to Bagging and AdaBoost algorithms on the higher performance.