Int. J. Bioinformatics Research and Applications, Vol. 7, No. 4, 2011 335 Copyright © 2011 Inderscience Enterprises Ltd. An optimised gene selection approach using wavelet power spectrum S. Prabakaran* Department of Computer Science and Engineering, SRM University, Kattankulathur, Chennai, India E-mail: Prabakaran.mani@gmail.com *Corresponding author Rajendra Sahu Indian Institute of Information Technology and Management, Gwalior, Madhya Pradesh, India E-mail: rsahu@iiitm.ac.in Sekhar Verma Indian Institute of Information Technology, Allahabad, India E-mail: sverma@iiita.ac.in Abstract: Data mining is a boon to many fields like bioinformatics for processing a vast amount of data. In our previous paper, we proposed a novel feature selection method for microarray data classification using Wavelet Power Spectrum (WPS). In this paper, we present optimisation techniques to improve the quality of the features thus selected and to select ‘tight genes’ from various cancer microarrays. The results show that ‘tight genes’ thus selected were more qualitative and could be used for a wide variety of data sets. Also, ‘tight genes’ thus selected in this mining process could be used with any existing classification approach. Keywords: DNA microarrays; microarray; data mining; feature selection; tight genes; RPV. Reference to this paper should be made as follows: Prabakaran, S., Sahu, R. and Verma, S. (2011) ‘An optimised gene selection approach using wavelet power spectrum’, Int. J. Bioinformatics Research and Applications, Vol. 7, No. 4, pp.335–354. Biographical notes: S. Prabakaran is a Professor in Computer Science Department at SRM University, Chennai. He gained his Master’s Degree in Computer Science and Engineering from Thappar University, Patiala, India, and PhD from IIITM, Gwalior. His research interests are in the areas of data mining, machine learning and biomedical informatics. Rajendra Sahu is a Professor at the Indian Institute of Information Technology and Management, Gwalior. He obtained PhD from IIT, Kharagpur. His research interests are machine learning and knowledge management.