On the Comparison of Classifiers for Microarray Data

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

Journal Name: Current Bioinformatics

Volume 5 , Issue 1 , 2010

Become EABM
Become Reviewer


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.

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

promotion: free to download

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2010
Page: [29 - 39]
Pages: 11
DOI: 10.2174/157489310790596376

Article Metrics

PDF: 31