  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.
