When is the majority-vote classifier beneficial?
Abstract
In his seminal work, Schapire (1990) proved that weak classifiers could be improved to achieve arbitrarily high accuracy, but he never implied that a simple majority-vote mechanism could always do the trick. By comparing the asymptotic misclassification error of the majority-vote classifier with the average individual error, we discover an interesting phase-transition phenomenon. For binary classification with equal prior probabilities, our result implies that, for the majority-vote mechanism to work, the collection of weak classifiers must meet the minimum requirement of having an average true positive rate of at least 50% and an average false positive rate of at most 50%.
Cite
@article{arxiv.1307.6522,
title = {When is the majority-vote classifier beneficial?},
author = {Mu Zhu},
journal= {arXiv preprint arXiv:1307.6522},
year = {2013}
}
Comments
Submitted to "The American Statistician", January 2013; revised, July 2013