A comparative study of classification methods for microarray data analysis

Authors:
Hong Hu;Jiuyong Li;Ashley Plank;Hua Wang;Grant Daggard
Affiliations:
University of Southern Queensland,, Toowoomba, QLD, Australia;University of Southern Queensland,, Toowoomba, QLD, Australia;University of Southern Queensland,, Toowoomba, QLD, Australia;University of Southern Queensland,, Toowoomba, QLD, Australia;University of Southern Queensland,, Toowoomba, QLD, Australia
Venue:
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Year:
2006

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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.