A comparative study of classification methods for microarray data analysis

Hu, Hong and Li, Jiuyong and Plank, Ashley and Wang, Hua and Daggard, Grant (2006) A comparative study of classification methods for microarray data analysis. In: 5th Australasian Data Mining Conference (AusDM 2006), 29-30 Nov 2006, Sydney, Australia.

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Abstract

In response to the rapid development of DNA Microarray technology, many classification methods have been used for Microarray classification. SVMs, decision trees, Bagging, Boosting and Random Forest are commonly used methods. In this paper, we conduct experimental comparison of LibSVMs, C4.5, BaggingC4.5, AdaBoostingC4.5, and Random Forest on seven Microarray cancer data sets. The experimental results show that all ensemble methods outperform C4.5. The experimental results also show that all five methods benefit from data preprocessing, including gene selection and discretization, in classification accuracy. In addition to comparing the average accuracies of ten-fold cross validation tests on seven data sets, we use two statistical tests to validate findings. We observe that Wilcoxon signed rank test is better than sign test for such purpose.


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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)
Depositing User: Dr Jiuyong (John) Li
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: microarray data, classification
Fields of Research (FOR2008): 01 Mathematical Sciences > 0104 Statistics > 010401 Applied Statistics
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/2095

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