Selecting a number of voters for a voting ensemble

2021/04/23 by Eric Bax, Bax, Eric · 1 citation
Computer Science · Decision Sciences · Mathematics · #Auction Theory and Applications #Data Stream Mining Techniques #Imbalanced Data Classification Techniques #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.2104.11833

arxiv created 2021/04/23 · arxiv updated 2021/04/27

Abstract

For a voting ensemble that selects an odd-sized subset of the ensemble classifiers at random for each example, applies them to the example, and returns the majority vote, we show that any number of voters may minimize the error rate over an out-of-sample distribution. The optimal number of voters depends on the out-of-sample distribution of the number of classifiers in error. To select a number of voters to use, estimating that distribution then inferring error rates for numbers of voters gives lower-variance estimates than directly estimating those error rates.

Citations

• A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting

Cited by

• Ensemble Performance Through the Lens of Linear Independence of Classifier Votes in Data Streams

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