A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification

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 ...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Alexander R. Statnikov, Lily Wang, Constantin F. Aliferis
• Keywords: BMCBI 2008, Gene Expression, Gene Expression Microarrays, Vector Machines