A maximally diversified multiple decision tree algorithm for microarray data classification
Paper
| Paper/Presentation Title | A maximally diversified multiple decision tree algorithm for microarray data classification |
|---|---|
| Presentation Type | Paper |
| Authors | Hu, Hong (Author), Li, Jiuyong (Author), Wang, Hua (Author), Daggard, Grant (Author) and Shi, Mingren (Author) |
| Editors | Boden, Mikael and Bailey, Timothy |
| Journal or Proceedings Title | Intelligent Systems For Bioinformatics 2006 |
| Number of Pages | 4 |
| Year | 2006 |
| Place of Publication | Sydney, Australia |
| ISBN | 1920682426 |
| Conference/Event | Workshop on Intelligent Systems for Bioinformatics (2006) |
| Event Details | Workshop on Intelligent Systems for Bioinformatics (2006) Event Date 04 Dec 2006 Event Location Hobart, Australia |
| 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. |
| Keywords | ensemble classifier, diversified classifiers, decision tree, Microarray data |
| ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
| 310505. Gene expression (incl. microarray and other genome-wide approaches) | |
| Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
| Byline Affiliations | Department of Mathematics and Computing |
| Department of Biological and Physical Sciences |
https://research.usq.edu.au/item/9y0y0/a-maximally-diversified-multiple-decision-tree-algorithm-for-microarray-data-classification
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