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. We then present three enhancements: 1) Classy Cluster Ensemble, which combines Classy Ensemble and cluster-based pruning; 2) Deep Learning experiments, showing the merits of Classy Ensemble over four image datasets: Fashion MNIST, CIFAR10, CIFAR100, and ImageNet; and 3) Classy Evolutionary Ensemble, wherein an evolutionary algorithm is used to select the set of models which Classy Ensemble picks from.

翻译：我们提出Classy Ensemble，一种针对分类任务的新颖集成生成算法，该算法通过基于每类准确率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优于两种著名的聚合算法——基于排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。随后我们提出三项改进：1) Classy Cluster Ensemble，将Classy Ensemble与基于聚类的剪枝相结合；2) 深度学习实验，展示了Classy Ensemble在四个图像数据集（Fashion MNIST、CIFAR10、CIFAR100和ImageNet）上的优势；3) Classy Evolutionary Ensemble，利用进化算法选择Classy Ensemble所依赖的模型集合。