IJCSI International Journal of Computer Science Issues, vol.9, no.6, pp.355-358, 2012 (Peer-Reviewed Journal)
Leukemia is one of the most common cancer type, and its diagnosis and classification is becoming increasingly complex and important. Here, we used a gene expression dataset and adapted bagging support vector machines (bSVM) for leukemia classification. bSVM trains each SVM seperately using bootstrap technique, then aggregates the performances of each SVM by majority voting. bSVM showed accuracy between 87.5% - 92.5%, area under ROC curve between 98.0% - 99.2%, F-measure between 90.5% - 92.7% and outperformed single SVM and other classification methods. We also compared our results with other study results which used the same dataset for leukemia classification. Experimental results revealed that bSVM showed the best performance and can be used as a biomarker for the diagnose of leukemia disease.