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.
|Item Type:||Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)|
|Additional Information:||Deposited in accordance with the copyright policy of the publisher (ACS Press). Published in the CRPIT series of the Australian Computer Society.|
|Uncontrolled Keywords:||ensemble classifier, diversified classifiers, decision tree, Microarray data|
|Subjects:||270000 Biological Sciences > 270800 Biotechnology > 270899 Biotechnology not elsewhere classified
280000 Information, Computing and Communication Sciences > 280200 Artificial Intelligence and Signal and Image Processing > 280213 Other Artificial Intelligence
|Depositing User:||Dr Jiuyong (John) Li|
|Date Deposited:||11 Oct 2007 00:57|
|Last Modified:||02 Jul 2013 22:42|
Actions (login required)
|Archive Repository Staff Only|