Research Article Open Access

A Gene Selection Algorithm using Bayesian Classification Approach

Alok Sharma1 and Kuldip K. Paliwal2
  • 1 School of Engineering and Physics, Faculty of Science Technology and Environment, University of the South Pacific, Fiji
  • 2 Signal Processing Lab, School of Engineering, Faculty of Engineering and Information Technology, Griffith University, Australia


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.

American Journal of Applied Sciences
Volume 9 No. 1, 2012, 127-131


Submitted On: 27 September 2011 Published On: 24 November 2011

How to Cite: Sharma, A. & Paliwal, K. K. (2012). A Gene Selection Algorithm using Bayesian Classification Approach. American Journal of Applied Sciences, 9(1), 127-131.

  • 13 Citations



  • Bayesian classifier
  • classification accuracy
  • feature selection
  • existing techniques
  • Bayesian classification
  • selection algorithms
  • biological significance
  • still limited
  • tissue samples