Combined classifier algorithm for imbalanced datasets

In order to improve the performance of the minority class, a combined classifier algorithm is presented based on data pre- processing. Firstly, Tomek links method is applied to preprocess a dataset, in which all Tomek links data points are removed to form a new dataset. Then the data points ofthe majority ctass in the new dataset are split into several disjoint subsets according to the imbalanced ratio, and each subset is combined with minority class to form a new training dataset. Finally, each training dataset is trained by least squares support vector machine(LS-SVM), and all of the LS-SVM classifiers are combined to form a classifying system. The label of a new testing data point is determined based on the voting strategy. The experimental results show that the proposed algorithm performs better than LS-SVM, synthetic minority over-sampling technique(SMOTE)and under-sampling(US)in terms of the classification performance of the majority class and the minority one.