We present Classy Ensemble, a novel ensemble-generation algorithm for classification tasks, which aggregates models through a weighted combination of per-class accuracy. Tested over 153 machine learning datasets we demonstrate that Classy Ensemble outperforms two other well-known aggregation algorithms -- order-based pruning and clustering-based pruning -- as well as the recently introduced lexigarden ensemble generator. Classy Ensemble also fares favorably with deep networks, over four image datasets: Fashion MNIST, CIFAR10, CIFAR100, and ImageNet.

翻译：我们提出Classy Ensemble，一种面向分类任务的新型集成生成算法，该算法通过基于每个类别精度的加权组合来聚合模型。在153个机器学习数据集上的测试表明，Classy Ensemble的性能优于两种广为人知的聚合算法——基于序的剪枝和基于聚类的剪枝——以及近期提出的lexigarden集成生成器。此外，在Fashion MNIST、CIFAR10、CIFAR100和ImageNet四个图像数据集上，Classy Ensemble与深度网络相比同样表现优越。