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On the Comparison of Classifiers for Microarray Data

[ Vol. 5 , Issue. 1 ]


Blaise Hanczar and Edward R. Dougherty   Pages 29 - 39 ( 11 )


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.


Microarray classification, error estimation, classifier comparison, variance study


LIPADE, University Paris Descartes, 45 rue des Saint-Peres, 75006 Paris, France.

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