A Gene Selection Algorithm using Bayesian Classification Approach
Alok Sharma and Kuldip K. Paliwal
DOI : 10.3844/ajassp.2012.127.131
American Journal of Applied Sciences
Volume 9, Issue 1
In this study, we propose a new feature (or gene) selection algorithm using Bayes classification approach. The algorithm can find gene subset crucial for cancer classification problem. Problem statement: Gene identification plays important role in human cancer classification problem. Several feature selection algorithms have been proposed for analyzing and understanding influential genes using gene expression profiles. Approach: The feature selection algorithms aim to explore genes that are crucial for accurate cancer classification and also endure biological significance. However, the performance of the algorithms is still limited. In this study, we propose a feature selection algorithm using Bayesian classification approach. Results: This approach gives promising results on gene expression datasets and compares favorably with respect to several other existing techniques. Conclusion: The proposed gene selection algorithm using Bayes classification approach is shown to find important genes that can provide high classification accuracy on DNA microarray gene expression datasets.
© 2012 Alok Sharma and Kuldip K. Paliwal. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.