A comparative study of two novel predictor set scoring methods

Authors:
Chia Huey Ooi;Madhu Chetty
Affiliations:
School of Computing and Information Technology, Monash University, Churchill, Australia;School of Computing and Information Technology, Monash University, Churchill, Australia
Venue:
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Year:
2005

Citing 3
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Abstract

Due to the large number of genes measured in a typical microarray dataset, feature selection plays an essential role in tumor classification. In turn, relevance and redundancy are key components in determining the optimal predictor set. However, a third component – the relative weights given to the first two also assumes an equal, if not greater importance in feature selection. Based on this third component, we developed two novel feature selection methods capable of producing high, unbiased classification accuracy in multiclass microarray dataset. In an in-depth analysis comparing the two methods, the optimal values of the relative weights are also estimated.