An extensive comparison of recent classification tools applied to microarray data

Since most classi%cation articles have applied a single technique to a single gene expression dataset, it is crucial to assess the performance of each method through a comprehensive comparative study. We evaluate by extensive comparison study extending Dudoit et al. (J. Amer. Statist. Assoc. 97 (2002) 77) the performance of recently developed classi%cation methods in microarray experiment, and provide the guidelines for %nding the most appropriate classi%cation tools in various situations. We extend their comparison in three directions: more classi%cation methods (21 methods), more datasets (7 datasets) and more gene selection techniques (3 methods). Our comparison study shows several interesting facts and provides the biologists and the biostatisticians some insights into the classi%cation tools in microarray data analysis. This study also shows that the more sophisticated classi%ers give better performances than classical methods such as kNN, DLDA, DQDA and the choice of gene selection method has much e>ect on the performance of the classi%cation methods, and thus the classi%cation methods should be considered together with the gene selection criteria. c © 2004 Elsevier B.V. All rights reserved.