A novel support vector sampling technique to improve classification accuracy and to identify key genes of leukaemia and prostate cancers

摘要

By extracting significant samples (which we refer to as support vector samples as they are located only on support vectors), we can identify principal genes and then use these genes to classify cancers either by support vector machines (SVM) or back-propagation neural networking (BPNN). We call this approach the support vector sampling technique (SVST). No matter the number of genes selected, our SVST method shows a significant improvement of classification performance. Our SVST method has averages 2-3% better performance when applied to leukemia and 6-7% better performance when applied to prostate cancer.