Multi-Test decision trees for gene expression data analysis

Authors:
Marcin Czajkowski;Marek Grze$#347/;Marek Kretowski
Affiliations:
Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland;School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada;Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland
Venue:
SIIS'11 Proceedings of the 2011 international conference on Security and Intelligent Information Systems
Year:
2011

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Abstract

This paper introduces a new type of decision trees which are more suitable for gene expression data. The main motivation for this work was to improve the performance of decision trees under a possibly small increase in their complexity. Our approach is thus based on univariate tests, and the main contribution of this paper is the application of several univariate tests in each non-terminal node of the tree. In this way, obtained trees are still relatively easy to analyze and understand, but they become more powerful in modelling high dimensional microarray data. Experimental validation was performed on publicly available gene expression datasets. The proposed method displayed competitive accuracy compared to the commonly applied decision tree methods.