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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Related Experiment Video

Updated: Jun 23, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

An empirical study of univariate and genetic algorithm-based feature selection in binary classification with

Michael Lecocke1, Kenneth Hess

  • 1Department of Mathematics, St. Mary's University, San Antonio, Texas 78228, USA.

Cancer Informatics
|May 22, 2009
PubMed
Summary
This summary is machine-generated.

Comparing feature selection methods for microarray data, this study found comparable misclassification error rates between univariate and multivariate approaches. Multivariate methods showed lower optimism bias but higher selection bias in binary classification tasks.

Keywords:
cross-validationfeature selectiongenetic algorithmsupervised-learning

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Feature selection is crucial for binary classification using microarray data.
  • Evaluating univariate versus multivariate approaches aims to improve misclassification error rates by considering jointly significant gene subsets without overfitting.

Purpose of the Study:

  • To empirically compare univariate and multivariate feature selection methods for binary classification with microarray data.
  • To assess if multivariate methods offer superior performance over univariate methods in terms of misclassification error rates.

Main Methods:

  • An empirical study utilizing 10-fold cross-validation applied externally to univariate and two multivariate (genetic algorithm-based) feature selection processes.
  • Application of these procedures across three supervised learning algorithms and six published two-class microarray datasets.

Main Results:

  • Average 10-fold external cross-validation error rates were comparable: 14.2% (univariate), 14.6% (single-stage GA), and 14.2% (two-stage GA).
  • Genetic algorithm (GA) analyses exhibited half the optimism bias but 2.5 times the selection bias compared to the univariate approach.

Conclusions:

  • 10-fold external cross-validation misclassification error rates were highly comparable across all tested methods.
  • A two-stage genetic algorithm approach did not provide a significant advantage over a single-stage approach.
  • The univariate approach demonstrated higher optimism bias and lower selection bias relative to both GA approaches.