Majority Voting by Independent Classifiers Can Increase Error Rates

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2013-01-01
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Morris, Max
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Vardeman, Stephen
University Professor Emeritus
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Statistics
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Industrial and Manufacturing Systems Engineering
The Department of Industrial and Manufacturing Systems Engineering teaches the design, analysis, and improvement of the systems and processes in manufacturing, consulting, and service industries by application of the principles of engineering. The Department of General Engineering was formed in 1929. In 1956 its name changed to Department of Industrial Engineering. In 1989 its name changed to the Department of Industrial and Manufacturing Systems Engineering.
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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.

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This is an Accepted Manuscript of an article published by Taylor & Francis in The American Statistician on March 25, 2013 available online: http://www.tandfonline.com/10.1080/00031305.2013.778788

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Tue Jan 01 00:00:00 UTC 2013
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