Campus Units

Industrial and Manufacturing Systems Engineering, Statistics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

2013

Journal or Book Title

The American Statistician

Volume

67

Issue

2

First Page

94

Last Page

96

DOI

10.1080/00031305.2013.778788

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.

Comments

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

Copyright Owner

American Statistical Association

Language

en

File Format

application/pdf

Published Version

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