# Ensemble Pruning based on Objection Maximization with a General Distributed Framework

arXiv: https://arxiv.org/abs/1806.04899 · HTML: https://mlsanity.com/paper/1806.04899/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2018.

## Built on by (most cited first, 8 of 8)

- [The Shapley Value of Classifiers in Ensemble Games](https://mlsanity.com/paper/2101.02153/) (2021)
- [Verification-Aided Deep Ensemble Selection](https://mlsanity.com/paper/2202.03898/) (2022)
- [Hierarchical Pruning of Deep Ensembles with Focal Diversity](https://mlsanity.com/paper/2311.10293/) (2023)
- [Improving Fairness with Ensemble Combination: Margin-Dependent Bounds](https://mlsanity.com/paper/2301.10813/) (2023)
- [Engineering the Neural Automatic Passenger Counter](https://mlsanity.com/paper/2203.01156/) (2022)
- [Deep Negative Correlation Classification](https://mlsanity.com/paper/2212.07070/) (2022)
- [Relearning ensemble selection based on new generated features](https://mlsanity.com/paper/2106.06761/) (2021)
- [Incentive Mechanism Design for Distributed Ensemble Learning](https://mlsanity.com/paper/2310.08792/) (2023)

## Abstract (arXiv)

Ensemble pruning, selecting a subset of individual learners from an original ensemble, alleviates the deficiencies of ensemble learning on the cost of time and space. Accuracy and diversity serve as two crucial factors while they usually conflict with each other. To balance both of them, we formalize the ensemble pruning problem as an objection maximization problem based on information entropy. Then we propose an ensemble pruning method including a centralized version and a distributed version, in which the latter is to speed up the former. At last, we extract a general distributed framework for ensemble pruning, which can be widely suitable for most of the existing ensemble pruning methods and achieve less time consuming without much accuracy degradation. Experimental results validate the efficiency of our framework and methods, particularly concerning a remarkable improvement of the execution speed, accompanied by gratifying accuracy performance.