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:我们提出了Classy Ensemble,这是一种用于分类任务的新型集成生成算法,它通过加权组合每个类别的准确率来聚合模型。经过153个机器学习数据集的测试,我们证明Classy Ensemble优于两种其他知名的聚合算法-基于顺序的剪枝和基于聚类的剪枝-以及最近推出的lexigarden集成生成器。然后,我们提出了三个增强功能:1)Classy Cluster Ensemble,它将Classy Ensemble和基于聚类的剪枝组合起来;2)Deep Learning实验,展示了在四个图像数据集中Classy Ensemble比较优越:时尚MNIST,CIFAR10,CIFAR100和ImageNet;以及3)Classy Evolutionary Ensemble,其中进化算法用于选择Classy Ensemble从中选择的模型集合。