Novel Bounds on the Probability of Misclassification in Majority Voting: Leveraging the Majority Size

A.T.J.R. Cobbenhagen (Corresponding author), A. Carè, M. C. Campi, F. A. Ramponi, D. J. Antunes, W.P.M.H. Heemels

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Majority voting is often employed as a tool to increase the robustness of data-driven decisions and control policies, a fact which calls for rigorous, quantitative evaluations of the limits and the potentials of majority voting schemes. This letter focuses on the case where the voting agents are binary classifiers and introduces novel bounds on the probability of misclassification conditioned on the size of the majority. We show that these bounds can be much smaller than the traditional upper bounds on the probability of misclassification. These bounds can be used in a 'Probably Approximately Correct' (PAC) setting, which allows for a practical implementation.

Original languageEnglish
Article number9272617
Pages (from-to)1513-1518
Number of pages6
JournalIEEE Control Systems Letters
Volume5
Issue number5
DOIs
Publication statusPublished - Nov 2021

Keywords

  • agents-based systems
  • Machine learning
  • statistical learning

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