Majority Voting by Independent Classifiers Can Increase Error Rates

Date
2013-01-01
Authors
Vardeman, Stephen
Morris, Max
Vardeman, Stephen
Journal Title
Journal ISSN
Volume Title
Publisher
Source URI
Altmetrics
Authors
Research Projects
Organizational Units
Statistics
Organizational Unit
Journal Issue
Series
Abstract

The technique of “majority voting” of classifiers is used in machine learning with the aim of constructing a new combined classification rule that has better characteristics than any of a given set of rules. The “Condorcet Jury Theorem” is often cited, incorrectly, as support for a claim that this practice leads to an improved classifier (i.e., one with smaller error probabilities) when the given classifiers are sufficiently good and are uncorrelated. We specifically address the case of two-category classification, and argue that a correct claim can be made for independent (not just uncorrelated) classification errors (not the classifiers themselves), and offer an example demonstrating that the common claim is false. Supplementary materials for this article are available online.

Description
<p>This is an Accepted Manuscript of an article published by Taylor & Francis in The American Statistician on March 25, 2013 available online: <a href="http://www.tandfonline.com/10.1080/00031305.2013.778788" target="_blank">http://www.tandfonline.com/10.1080/00031305.2013.778788</a></p>
Keywords
Citation
Collections