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Majority Voting by Independent Classifiers Can Increase Error Rates

机译：独立分类器的多数投票可能会增加错误率

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摘要

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.
机译：分类器的“多数投票”技术用于机器学习，目的是构造一种新的组合分类规则，该规则具有比给定规则集中的任何规则都更好的特征。通常会错误地引用“约定陪审团定理”，以支持一种说法，即当给定分类器足够好且不相关时，这种做法会导致改进的分类器（即错误概率较小的分类器）。我们专门针对两类分类的情况，并提出可以针对独立的（不仅是不相关的）分类错误（不是分类器本身）提出正确的主张，并提供一个示例来证明共同的主张是错误的。可在线获得本文的补充材料。

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  • 来源
    《The American statistician》 |2013年第2期|94-96|共3页
  • 作者

    Stephen B. Vardeman; Max D. Morris;

  • 作者单位

    Industrial and Manufacturing Systems Engineering Department, and Statistics Department, Iowa State University, 3004 Black Engineering Building, Ames, IA 50011-2164;

    Industrial and Manufacturing Systems Engineering Department, and Statistics Department, Iowa State University, 3004 Black Engineering Building, Ames, IA 50011-2164;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Classifier fusion; Condorcet Jury Theorem; Machine learning;

    机译：分类器融合;孔多塞陪审团定理;机器学习;

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