Promoting High Diversity Ensemble Learning with EnsembleBench

Yanzhao Wu, Ling Liu, Zhongwei Xie, Juhyun Bae, Ka-Ho Chow, Wenqi Wei
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引用次数: 7

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.
使用EnsembleBench促进高多样性集成学习
近年来,集成学习重新引起了人们的兴趣。本文介绍了EnsembleBench,这是一个评估和推荐高多样性和高精度集成的整体框架。EnsembleBench的设计提供了三个新功能:(1)EnsembleBench引入了一组定量指标,用于评估集成的质量,并用于比较为相同的学习任务构建的替代集成。(2) EnsembleBench实现了一套基线多样性指标和优化的多样性指标,用于识别和选择高多样性和高质量的集成,使其成为对标、评估和推荐高多样性模型集成的有效框架。(3)首次发布的EnsembleBench提供了四种具有代表性的集成一致性方法,可以对一致性方法对集成精度的影响进行实证研究。在流行的基准数据集上进行了全面的实验评估,证明了EnsembleBench在促进高多样性集成和提高所选集成整体性能方面的实用性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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