  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.
