论文标题
通过整合板促进高度多样性的合奏学习
Promoting High Diversity Ensemble Learning with EnsembleBench
论文作者
论文摘要
近年来，合奏学习正在获得新的利益。本文介绍了整体板，这是一个整体框架，用于评估和建议高度的多样性和高精度合奏。 Ensemblebench的设计提供了三个新颖的功能：（1）EnsemberBench引入了一组定量指标，用于评估合奏的质量，并比较针对相同学习任务构建的替代合奏。 （2）整体板上实现了一套基线多样性指标，并优化了多样性指标，以识别和选择具有高度多样性和高质量的合奏，从而成为基准测试，评估和推荐高度多样性模型模型集合的有效框架。 （3）在第一个集合板的首次发行中提供了四种代表性的集合共识方法，从而实现了关于共识方法对集合准确性的影响的经验研究。对流行基准数据集进行的全面实验评估表明，集成板对促进高度多样性合奏并提高选定合奏的整体性能的实用性和有效性。
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.
