Ensemble Pruning based on Objection Maximization with a General Distributed Framework

TL;DR

This paper introduces a novel ensemble pruning method based on objection maximization with a general distributed framework, significantly improving speed while maintaining accuracy.

Contribution

It formalizes ensemble pruning as an objection maximization problem and proposes a scalable distributed framework applicable to various pruning methods.

Findings

Significant reduction in execution time.

Maintains high accuracy levels.

Framework applicable to multiple pruning techniques.

Abstract

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.