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A novel hybrid approach of feature selection through feature clustering using microarray gene expression data
conference contributionposted on 2017-12-06, 00:00 authored by Choudhury WahidChoudhury Wahid, A B M Shawkat Ali, Kevin TickleKevin Tickle
Hybrid methods for feature selection comprised of combination of filter and wrapper approaches have recently been emerged as strong techniques for the problem in this domain. In this work we have proposed three simple hybrid approaches for reducing data dimensionality while maintaining classification accuracy which combine our basic feature selection through feature clustering (FSFC) approach to other standard approaches of feature selection in different orientation. We have employed popular ROC curve analysis to evaluate experimental outcome. Our experimental results clearly show suitability of our methods in hybrid approaches of feature selection in micro-array gene expression domain.
Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)
Parent TitleProceedings of the 11th International Conference on Hybrid Intelligent Systems (HIS 2011), Malacca, Malaysia, December 5-8, 2011.
Number of Pages6
Place of PublicationUSA
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External Author AffiliationsCentre for Intelligent and Networked Systems (CINS); Faculty of Arts, Business, Informatics and Education; Institute for Resource Industries and Sustainability (IRIS); School of Information and Communication Technology;