A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification
Open Access
- 22 July 2008
- journal article
- research article
- Published by Springer Science and Business Media LLC in BMC Bioinformatics
- Vol. 9 (1), 319
- https://doi.org/10.1186/1471-2105-9-319
Abstract
Background: Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain. Results: In the present paper we identify methodological biases of prior work comparing random forests and support vector machines and conduct a new rigorous evaluation of the two algorithms that corrects these limitations. Our experiments use 22 diagnostic and prognostic datasets and show that support vector machines outperform random forests, often by a large margin. Our data also underlines the importance of sound research design in benchmarking and comparison of bioinformatics algorithms. Conclusion: We found that both on average and in the majority of microarray datasets, random forests are outperformed by support vector machines both in the settings when no gene selection is performed and when several popular gene selection methods are used.Keywords
This publication has 21 references indexed in Scilit:
- Critical Review of Published Microarray Studies for Cancer Outcome and Guidelines on Statistical Analysis and ReportingJNCI Journal of the National Cancer Institute, 2007
- Multi-class feature selection for texture classificationPattern Recognition Letters, 2006
- GEMS: A system for automated cancer diagnosis and biomarker discovery from microarray gene expression dataInternational Journal of Medical Informatics, 2005
- Using permutations instead of student's t distribution for p-values in paired-difference algorithm comparisonsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- An extensive comparison of recent classification tools applied to microarray dataComputational Statistics & Data Analysis, 2005
- A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosisBioinformatics, 2004
- An Analytical Method for Multiclass Molecular Cancer ClassificationSIAM Review, 2003
- Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression DataJournal of the American Statistical Association, 2002
- Ensemble Methods in Machine LearningLecture Notes in Computer Science, 2000
- Improvements on Cross-Validation: The .632+ Bootstrap MethodJournal of the American Statistical Association, 1997