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CLASSIFICATION OF HIGH-DIMENSIONAL MICROARRAY DATA WITH A TWO-STEP PROCEDURE VIA A WILCOXON CRITERION AND MULTILAYER PERCEPTRON

VLADIMIR NIKULIN

Department of Mathematics, University of Queensland, Australia

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TIAN-HSIANG HUANG

Institute of Information Management, National Cheng Kung University, Taiwan

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GEOFFREY J. MCLACHLAN

Department of Mathematics and Institute for Molecular Bioscience, University of Queensland, Australia

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https://doi.org/10.1142/S1469026811002969Cited by:1 (Source: Crossref)

Abstract

The method presented in this paper is novel as a natural combination of two mutually dependent steps. Feature selection is a key element (first step) in our classification system, which was employed during the 2010 International RSCTC data mining (bioinformatics) Challenge. The second step may be implemented using any suitable classifier such as linear regression, support vector machine or neural networks. We conducted leave-one-out (LOO) experiments with several feature selection techniques and classifiers. Based on the LOO evaluations, we decided to use feature selection with the separation type Wilcoxon-based criterion for all final submissions. The method presented in this paper was tested successfully during the RSCTC data mining Challenge, where we achieved the top score in the Basic track.

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