Zhang, Zhongwei and Li, Jiuyong and Hu, Hong and Zhou, Hong (2010) A robust ensemble classification method analysis. In: 2009 International Conference on Bioinformatics and Computational Biology , 13-16 Jul 2009, Las Vegas, NV. United States.
PDF (Accepted Version - Chapter 17)
Apart from the dimensionality problem, the uncertainty of Microarray data quality is another major challenge of Microarray classification. Microarray data contains various levels of noise and quite often are high levels of noise, and these data lead to unreliable and low accuracy analysis as well as the high dimensionality problem. In this paper, we propose a new Microarray data classification method, based on diversified multiple trees. The new method contains features that, (1) make most use of the information from the abundant genes in the Microarray data, and (2) use a unique diversity measurement in the ensemble decision committee. The experimental results show that the proposed classification method (DMDT) and the well known method (CS4), which diversifies trees by using distinct tree roots, are more accurate on average than other well-known ensemble methods, including Bagging, Boosting and Random Forests. The experiments also indicate that using diversity measurement of DMDT improves the classification accuracy of ensemble classification on Microarray data.
|Item Type:||Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)|
|Additional Information:||Chapter 17. Accepted version deposited with blanket permission of publisher. Print copy held USQ Library 570.285 Adv.|
|Uncontrolled Keywords:||microarray gene data; classification method; ensemble decision tree; diversity; accuracy|
|Depositing User:||Dr Zhongwei Zhang|
|Date Deposited:||06 Jun 2011 23:11|
|Last Modified:||03 Jul 2013 00:05|
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