Investigating the class-specific relevance of predictor sets obtained from DDP-Based feature selection technique

Authors:
Chia Huey Ooi;Madhu Chetty;Shyh Wei Teng
Affiliations:
Gippsland School of Information Technology, Monash University, Churchill, Australia;Gippsland School of Information Technology, Monash University, Churchill, Australia;Gippsland School of Information Technology, Monash University, Churchill, Australia
Venue:
PRIB'06 Proceedings of the 2006 international conference on Pattern Recognition in Bioinformatics
Year:
2006

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Abstract

Feature selection is crucial to tumor classification due to the high dimensionality of microarray datasets. With the aid of the degree of differential prioritization (DDP) between relevance and antiredundancy, our proposed DDP-based feature selection technique is capable of achieving better accuracies than those reported in previous studies, while using fewer genes in the predictor set. Additionally, we discovered a strong correlation between the DDP parameter in our feature selection technique and the number of classes in the dataset. This leads us to question if the measure of relevance in our feature selection technique becomes less efficient at capturing the class-specific relevance for each individual class of the dataset as the number of classes increases. In this study, we analyze the class-specific relevance of the predictor sets found using our feature selection technique. The analysis ultimately lays down the theoretical foundation for a beneficial improvement to our feature selection technique.