Promoting High Diversity Ensemble Learning with EnsembleBench

TL;DR

EnsembleBench is a comprehensive framework designed to evaluate, compare, and recommend high diversity, high accuracy ensemble models using novel metrics, diverse consensus methods, and extensive empirical validation.

Contribution

It introduces new quantitative metrics for ensemble quality assessment and provides a platform for benchmarking and selecting diverse, high-performing ensemble models.

Findings

EnsembleBench effectively identifies high diversity, high accuracy ensembles.

Benchmarking results show improved ensemble performance using EnsembleBench metrics.

Empirical studies highlight the impact of consensus methods on ensemble accuracy.

Abstract

Ensemble learning is gaining renewed interests in recent years. This paper presents EnsembleBench, a holistic framework for evaluating and recommending high diversity and high accuracy ensembles. The design of EnsembleBench offers three novel features: (1) EnsembleBench introduces a set of quantitative metrics for assessing the quality of ensembles and for comparing alternative ensembles constructed for the same learning tasks. (2) EnsembleBench implements a suite of baseline diversity metrics and optimized diversity metrics for identifying and selecting ensembles with high diversity and high quality, making it an effective framework for benchmarking, evaluating and recommending high diversity model ensembles. (3) Four representative ensemble consensus methods are provided in the first release of EnsembleBench, enabling empirical study on the impact of consensus methods on ensemble accuracy. A comprehensive experimental evaluation on popular benchmark datasets demonstrates the utility and effectiveness of EnsembleBench for promoting high diversity ensembles and boosting the overall performance of selected ensembles.