A maximally diversified multiple decision tree algorithm for microarray data classification

Authors:
Hong Hu;Jiuyong Li;Hua Wang;Grant Daggard;Mingren Shi
Affiliations:
University of Southern Queensland, Toowoomba, QLD, Australia;University of Southern Queensland, Toowoomba, QLD, Australia;University of Southern Queensland, Toowoomba, QLD, Australia;University of Southern Queensland, Toowoomba, QLD, Australia;University of Southern Queensland, Toowoomba, QLD, Australia
Venue:
WISB '06 Proceedings of the 2006 workshop on Intelligent systems for bioinformatics - Volume 73
Year:
2006

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Abstract

We investigate the idea of using diversified multiple trees for Microarray data classification. We propose an algorithm of Maximally Diversified Multiple Trees (MDMT), which makes use of a set of unique trees in the decision committee. We compare MDMT with some well-known ensemble methods, namely AdaBoost, Bagging, and Random Forests. We also compare MDMT with a diversified decision tree algorithm, Cascading and Sharing trees (CS4), which forms the decision committee by using a set of trees with distinct roots. Based on seven Microarray data sets, both MDMT and CS4 are more accurate on average than AdaBoost, Bagging, and Random Forests. Based on a sign test of 95% confidence, both MDMT and CS4 perform better than majority traditional ensemble methods tested. We discuss differences between MDMT and CS4.