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.
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)|
|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)
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