Publication
AAMAS 2020
Conference paper

Voting with Random Classifiers (VORACE)

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Abstract

We propose an innovative ensemble technique that uses voting rules over a set of randomly-generated classifiers. Given a new input sample, we interpret the output of each classifier as a ranking over the set of possible classes. We then aggregate these output rankings using a voting rule, which treats them as preferences over the classes. We show that our approach obtains good results compared to the state-of-the-art, both providing a theoretical analysis and an empirical evaluation of the approach on several datasets.

Date

19 May 2020

Publication

AAMAS 2020

Authors

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