On the Comparison of Classifiers for Microarray Data

Title: On the Comparison of Classifiers for Microarray Data

Volume: 5 Issue: 1

Author(s): Blaise Hanczar and Edward R. Dougherty

Affiliation:

Keywords: Microarray classification, error estimation, classifier comparison, variance study

Abstract: 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.

Cite this article as:

Hanczar Blaise and Dougherty R. Edward, On the Comparison of Classifiers for Microarray Data, Current Bioinformatics 2010; 5 (1) . https://dx.doi.org/10.2174/157489310790596376