A support vector machine ensemble for cancer classification using gene expression data

In this paper, we propose a support vector machine (SVM) ensemble classification method. Firstly, dataset is preprocessed by Wilcoxon rank sum test to filter irrelevant genes. Then one SVM is trained using the training set, and is tested by the training set itself to get prediction results. Those samples with error prediction result or low confidence are selected to train the second SVM, and also the second SVM is tested again. Similarly, the third SVM is obtained using those samples, which cannot be correctly classified using the second SVM with large confidence. The three SVMs form SVM ensemble classifier. Finally, the testing set is fed into the ensemble classifier. The final test prediction results can be got by majority voting. Experiments are performed on two standard benchmark datasets: Breast Cancer, ALL/AML Leukemia. Experimental results demonstrate that the proposed method can reach the state-of-the-art performance on classification.