An extensive comparison of recent classification tools applied to microarray data

Since most classification 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 classification methods in microarray experiment, and provide the guidelines for finding the most appropriate classification tools in various situations. We extend their comparison in three directions: more classification 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 classification tools in microarray data analysis. This study also shows that the more sophisticated classifiers give better performances than classical methods such as kNN, DLDA, DQDA and the choice of gene selection method has much effect on the performance of the classification methods, and thus the classification methods should be considered together with the gene selection criteria.