Tissue classification with gene expression profiles
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Gene selection from microarray data for cancer classification-a machine learning approach
Computational Biology and Chemistry
A hybrid GA/SVM approach for gene selection and classification of microarray data
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Feature generation using genetic programming with application to fault classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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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.