On the Comparison of Classifiers for Microarray Data Academic Article uri icon


  • The aim of many microarray experiments is to build discriminatory diagnosis and prognosis models. A large number of supervised methods have been proposed in literature for microarray-based classification. Model comparison, which is based on the classification error estimation, is a critical issue. Previous studies have shown that error estimation is unreliable in high-dimensional small-sample settings. This leads naturally to questioning the validity of classificationrule comparison approaches being used in the literature. In this paper we present a brief review of the different comparison methods used in bioinformatics. Then, we test these methods on a set of simulations based on both synthetic and real data. These simulations include different feature-label distributions, classification rules, error estimators and variance estimators. The results show that none of these methods can provide reliable comparison across a wide spectrum of feature-label distributions and classification rules. 2010 Bentham Science Publishers Ltd.

published proceedings


author list (cited authors)

  • Hanczar, B., & Dougherty, E. R.

citation count

  • 13

complete list of authors

  • Hanczar, Blaise||Dougherty, Edward R

publication date

  • March 2010