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

作者：Chen Austin H^*; Lin Ching Heng

来源：Expert Systems with Applications, 2011, 38(4): 3209-3219.

DOI：10.1016/j.eswa.2010.09.009

• Machine learning
• Artificial intelligence
• Biomedicine
• Gene selection
• Cancer classification
• SVM
• Leukaemia
• Prostate cancer

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.

• 出版日期2011-4
• 单位河北医科大学