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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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.
|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)|
|Uncontrolled Keywords:||microarray data, classification|
|Subjects:||270000 Biological Sciences > 270800 Biotechnology > 270899 Biotechnology not elsewhere classified
280000 Information, Computing and Communication Sciences
|Depositing User:||Dr Jiuyong (John) Li|
|Date Deposited:||11 Oct 2007 00:57|
|Last Modified:||02 Jul 2013 22:42|
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