Are random forests better than support vector machines for microarray-based cancer classification?
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.
Statnikov
Alexander
A
Discovery Systems Laboratory, Vanderbilt University, Nashville, TN, USA.
Aliferis
Constantin F
CF
D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D003658:Decision Making, Computer-Assisted; D003663:Decision Trees; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000145:classification / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods