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In many machine learning scenarios, looking for the best classifier that fitsa particular dataset can be very costly in terms of time and resources.Moreover, it can require deep knowledge of the specific domain. We propose anew technique which does not require profound expertise in the domain andavoids the commonly used strategy of hyper-parameter tuning and modelselection. Our method is an innovative ensemble technique that uses votingrules 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 ofpossible classes. We then aggregate these output rankings using a voting rule,which treats them as preferences over the classes. We show that our approachobtains good results compared to the state-of-the-art, both providing atheoretical analysis and an empirical evaluation of the approach on severaldatasets.

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