# SPOCC: Scalable POssibilistic Classifier Combination -- toward robust aggregation of classifiers

arXiv: https://arxiv.org/abs/1908.06475 · HTML: https://mlsanity.com/paper/1908.06475/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [Test-time Collective Prediction](https://mlsanity.com/paper/2106.12012/) (2021)

## Abstract (arXiv)

We investigate a problem in which each member of a group of learners is trained separately to solve the same classification task. Each learner has access to a training dataset (possibly with overlap across learners) but each trained classifier can be evaluated on a validation dataset. We propose a new approach to aggregate the learner predictions in the possibility theory framework. For each classifier prediction, we build a possibility distribution assessing how likely the classifier prediction is correct using frequentist probabilities estimated on the validation set. The possibility distributions are aggregated using an adaptive t-norm that can accommodate dependency and poor accuracy of the classifier predictions. We prove that the proposed approach possesses a number of desirable classifier combination robustness properties.