# End-to-End Neural Network Training for Hyperbox-Based Classification

arXiv: https://arxiv.org/abs/2307.09269 · HTML: https://mlsanity.com/paper/2307.09269/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 18 Jul 2023.

## Builds on (most cited first, 3 of 3)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Hyperbox based machine learning algorithms: A comprehensive survey](https://mlsanity.com/paper/1901.11303/) (2019)
- [REDS: Rule Extraction for Discovering Scenarios](https://mlsanity.com/paper/1910.01713/) (2019)

## Abstract (arXiv)

Hyperbox-based classification has been seen as a promising technique in which decisions on the data are represented as a series of orthogonal, multidimensional boxes (i.e., hyperboxes) that are often interpretable and human-readable. However, existing methods are no longer capable of efficiently handling the increasing volume of data many application domains face nowadays. We address this gap by proposing a novel, fully differentiable framework for hyperbox-based classification via neural networks. In contrast to previous work, our hyperbox models can be efficiently trained in an end-to-end fashion, which leads to significantly reduced training times and superior classification results.