Voting with Random Classifiers (VORACE): Theoretical and Experimental Analysis
Abstract
In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique which does not require profound expertise in the domain and avoids the commonly used strategy of hyper-parameter tuning and model selection. Our method is 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.
Cite
@article{arxiv.1909.08996,
title = {Voting with Random Classifiers (VORACE): Theoretical and Experimental Analysis},
author = {Cristina Cornelio and Michele Donini and Andrea Loreggia and Maria Silvia Pini and Francesca Rossi},
journal= {arXiv preprint arXiv:1909.08996},
year = {2022}
}