End-to-End Neural Network Training for Hyperbox-Based Classification
Abstract
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.
Cite
@article{arxiv.2307.09269,
title = {End-to-End Neural Network Training for Hyperbox-Based Classification},
author = {Denis Mayr Lima Martins and Christian Lülf and Fabian Gieseke},
journal= {arXiv preprint arXiv:2307.09269},
year = {2023}
}
Comments
6 pages, accepted for poster presentation at ESANN 2023