%0 Journal Article
	%A Khin May Win and  Nan Sai Moon Kham
	%D 2008
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 18, 2008
	%T Feature Subset Selection approach based on Maximizing Margin of Support Vector Classifier
	%U https://publications.waset.org/pdf/15714
	%V 18
	%X Identification of cancer genes that might anticipate
the clinical behaviors from different types of cancer disease is
challenging due to the huge number of genes and small number of
patients samples. The new method is being proposed based on
supervised learning of classification like support vector machines
(SVMs).A new solution is described by the introduction of the
Maximized Margin (MM) in the subset criterion, which permits to
get near the least generalization error rate. In class prediction
problem, gene selection is essential to improve the accuracy and to
identify genes for cancer disease. The performance of the new
method was evaluated with real-world data experiment. It can give
the better accuracy for classification.
	%P 1939 - 1944