Integrating feature selection and Min-Max Modular SVM for powerful ensemble

Min-Max Modular Support Vector Machine (M3-SVM) is a powerful ensemble learning method for large scale data processing, which consists of the data decomposition and min-max combination rule. However, when the data contains many redundant or irrelevant features, the ensemble learning performance of M3-SVM will degrade. To address this issue, reduce the computation complexity and enhance the diversity among base classifiers, we propose a method that the feature selection is integrated to the M3-SVM using two integration models. In order to understand the effect of feature selection for ensemble learning, the diversity among base classifiers caused by feature selection is also explored. Experimental results on two large scale data sets including one imbalance data set show that the proposed M3-SVM with feature selection can gain a better performance and higher diversity than original one.