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

arXiv: https://arxiv.org/abs/1909.08996 · HTML: https://mlsanity.com/paper/1909.08996/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2019.

## Built on by (most cited first, 5 of 5)

- [When Is It Acceptable to Break the Rules? Knowledge Representation of Moral Judgement Based on Empirical Data](https://mlsanity.com/paper/2201.07763/) (2022)
- [Deep ensembles in bioimage segmentation](https://mlsanity.com/paper/2112.12955/) (2021)
- [SenTag: a Web-based Tool for Semantic Annotation of Textual Documents](https://mlsanity.com/paper/2110.15062/) (2021)
- [Liquid Democracy for Low-Cost Ensemble Pruning](https://mlsanity.com/paper/2401.17443/) (2024)
- [Liquid Ensemble Selection for Continual Learning](https://mlsanity.com/paper/2405.07327/) (2024)

## Abstract (arXiv)

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.