  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.
