Voting with Random Classifiers (VORACE): Theoretical and Experimental Analysis
This addresses the need for efficient and accessible machine learning solutions for users without deep domain expertise, though it is incremental in its approach.
The paper tackles the problem of costly and expertise-dependent classifier selection by proposing VORACE, an ensemble method that aggregates rankings from randomly-generated classifiers using voting rules, achieving competitive results compared to state-of-the-art methods.
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.