Sciweavers

Share
BMCBI
2008

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

8 years 10 months ago
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 ...
Alexander R. Statnikov, Lily Wang, Constantin F. A
Added 08 Dec 2010
Updated 08 Dec 2010
Type Journal
Year 2008
Where BMCBI
Authors Alexander R. Statnikov, Lily Wang, Constantin F. Aliferis
Comments (0)
books