Classy Ensemble: A Novel Ensemble Algorithm for Classification
This is an incremental improvement for researchers and practitioners in machine learning, offering a new ensemble method that enhances classification accuracy.
The paper tackles the problem of ensemble generation for classification by introducing Classy Ensemble, which aggregates models based on per-class accuracy, and shows it outperforms existing algorithms across 153 datasets and four image datasets, with enhancements like evolutionary selection improving performance on the hardest dataset.
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.