Indian Journal of Science and Technology
Year: 2015, Volume: 8, Issue: 26, Pages: 1-6
Sang-Hong Lee1 and Joon S. Lim2*
1 Department of Computer Science and Engineering, Anyang University, Anyang-si - 708113, Republic of Korea; [email protected]
2 IT College, Gachon University, Seongnam-si, Republic of Korea; [email protected]
In this paper, we propose a supervised gene selection method to classify tumor and normal samples based on the Bounded Sum of Weighted Fuzzy Membership Functions (BSWFM). This study compares the performance of a Neural Network with a Weighted Fuzzy Membership Function (NEWFM) with and without the proposed gene selection method. The superiority of the NEWFM with gene selection over the one without gene selection was demonstrated using a colon cancer dataset. Two thousand genes were used as inputs for the NEWFM without gene selection, and these resulted in accuracy, specificity, and sensitivity of 79%, 59.1% and 90%, respectively. A minimum of 19 genes were used as inputs for the NEWFM with gene selection, and these resulted in accuracy, specificity, and sensitivity of 87.4%, 72.7% and 95%, respectively. The results show that the NEWFM with gene selection performed better than the one without gene selection.
Keywords: BSWFM, Distance, Fuzzy Neural Network, Gene Selection, NEWFM
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