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A Maximally Diversified Multiple Decision Tree Algorithm for Microarray Data Classification

Hu, H., Li, J., Wang, H., Daggard, G. and Shi, M.

    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.
Cite as: Hu, H., Li, J., Wang, H., Daggard, G. and Shi, M. (2006). A Maximally Diversified Multiple Decision Tree Algorithm for Microarray Data Classification. In Proc. 2006 Workshop on Intelligent Systems for Bioinformatics (WISB 2006), Hobart, Australia. CRPIT, 73. Boden, M. and Bailey, T. L., Eds. ACS. 35-38.
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