Increasing classification accuracy by combining adaptive sampling and convex pseudo-data

Authors:
Chia Huey Ooi;Madhu Chetty
Affiliations:
Gippsland School of Computing and Information Technology, Monash University, Churchill, VIC, Australia;Gippsland School of Computing and Information Technology, Monash University, Churchill, VIC, Australia
Venue:
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2005

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Abstract

The availability of microarray data has enabled several studies on the application of aggregated classifiers for molecular classification. We present a combination of classifier aggregating and adaptive sampling techniques capable of increasing prediction accuracy of tumor samples for multiclass datasets. Our aggregated classifier method is capable of improving the classification accuracy of predictor sets obtained from our maximal-antiredundancy-based feature selection technique. On the Global Cancer Map (GCM) dataset, an improvement over the highest accuracy reported has been achieved by the joint application of our feature selection technique and the modified aggregated classifier method.