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. This latter, combining learning and evolution, resulted in improved performance on the hardest dataset.

翻译：我们提出Classy Ensemble，这是一种用于分类任务的新颖集成生成算法，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优于两种众所周知的聚合算法——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，融合了Classy Ensemble与基于聚类的剪枝；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所取样的模型集。后者将学习与进化相结合，在最难的数据集上取得了更优的性能。