Machine Learning
Machine Learning - Special issue on inductive transfer
Random Forests for multiclass classification: Random MultiNomial Logit
Expert Systems with Applications: An International Journal
IEEE Transactions on Information Technology in Biomedicine
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Microarray-based gene expression profiling has been a promising approach in predicting cancer classification and prognosis outcomes over the past few years. In this paper, we have implemented a systematic method that can improve cancer classification. By extracting significant samples (which we refer to as support vector samples because they are located only on support vectors), we allow the back propagation neural networking (BPNN) to learn more tasks. The proposed method named as the multi-task support vector sample learning (MTSVSL) technique. We demonstrate experimentally that the genes selected by our MTSVSL method yield superior classification performance with application to leukemia and prostate cancer gene expression datasets. Our proposed MTSVSL method is a novel approach which is expedient and perform exceptionally well in cancer diagnosis and clinical outcome prediction. Our method has been successfully applied to cancer type-based classifications on microarray gene expression datasets, and furthermore, MTSVSL improves the accuracy of traditional BPNN technique.