# Classy Ensemble: A Novel Ensemble Algorithm for Classification

arXiv: https://arxiv.org/abs/2302.10580 · HTML: https://mlsanity.com/paper/2302.10580/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 21 Feb 2023.

## Builds on (most cited first, 5 of 5)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [PMLB v1.0: An open source dataset collection for benchmarking machine learning methods](https://mlsanity.com/paper/2012.00058/) (2020)
- [Symbolic-Regression Boosting](https://mlsanity.com/paper/2206.12082/) (2022)
- [EC-KitY: Evolutionary Computation Tool Kit in Python with Seamless Machine Learning Integration](https://mlsanity.com/paper/2207.10367/) (2022)
- [Combining Deep Learning with Good Old-Fashioned Machine Learning](https://mlsanity.com/paper/2207.03757/) (2022)

## Built on by (most cited first, 1 of 1)

- [A Melting Pot of Evolution and Learning](https://mlsanity.com/paper/2306.04971/) (2023)

## Abstract (arXiv)

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.