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arXiv · 1909.08996

Voting with Random Classifiers (VORACE): Theoretical and Experimental Analysis

Cristina Cornelio·Michele Donini·Andrea Loreggia·Maria Silvia Pini·Francesca Rossi
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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.

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Cristina Cornelio, Michele Donini, Andrea Loreggia, Maria Silvia Pini, Francesca Rossi. 2019-09-18. Voting with Random Classifiers (VORACE): Theoretical and Experimental Analysis. https://doi.org/10.1007/s10458-021-09504-y

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