A maximally diversified multiple decision tree algorithm for microarray data classification

Hu, Hong and Li, Jiuyong and Wang, Hua and Daggard, Grant and Shi, Mingren (2006) A maximally diversified multiple decision tree algorithm for microarray data classification. In: Workshop on Intelligent Systems for Bioinformatics, 4 Dec 2006, Hobart, Australia.


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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.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Deposited in accordance with the copyright policy of the publisher (ACS Press). Published in the CRPIT series of the Australian Computer Society.
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 11 Oct 2007 00:57
Last Modified: 02 Jul 2013 22:42
Uncontrolled Keywords: ensemble classifier, diversified classifiers, decision tree, Microarray data
Fields of Research : 08 Information and Computing Sciences > 0899 Other Information and Computing Sciences > 089999 Information and Computing Sciences not elsewhere classified
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
06 Biological Sciences > 0604 Genetics > 060405 Gene Expression (incl. Microarray and other genome-wide approaches)
URI: http://eprints.usq.edu.au/id/eprint/2097

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