We have located links that may give you full text access.
Comparative Study
Journal Article
Validation Studies
Are random forests better than support vector machines for microarray-based cancer classification?
AMIA ... Annual Symposium Proceedings 2007 October 12
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 decision support 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 found that random forest classifiers outperform support vector machines. In the present paper we point to several biases of this prior work and conduct a new unbiased evaluation of the two algorithms. Our experiments using 18 diagnostic and prognostic datasets show that support vector machines outperform random forests often by a large margin.
Full text links
Related Resources
Trending Papers
Review article: Recent advances in ascites and acute kidney injury management in cirrhosis.Alimentary Pharmacology & Therapeutics 2024 March 26
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app