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.
The code for this project can be found in the following Github repository.
References
2023
End-to-End Neural Network Training for Hyperbox-Based Classification
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.
@inproceedings{esann,author={Martins, Denis Mayr Lima and Lülf, Christian and Gieseke, Fabian},title={End-to-End Neural Network Training for Hyperbox-Based Classification},booktitle={31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, {ESANN}},year={2023},}
2024
Training Neural Networks End-to-End for Hyperbox-Based Classification
Modern decision-making requires the use of powerful algorithms to make sense of a variety of data. In this context, hyperbox induction 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 hyperbox induction methods are no longer capable of efficiently handling the increasing volumes of data many application domains are confronted with. Moreover, current methods offer little to no control on specific properties of the induced box models, such as the number or the sizes of the hyperboxes. In this work, we propose a novel, fully differentiable framework for hyperbox induction that makes use of recent advancement in neural networks. In contrast to existing approaches, our hyperbox-based models can be trained in an end-to-end fashion, which leads to significantly reduced training times and superior classification results.
@article{neurocom,author={Martins, Denis Mayr Lima and Lülf, Christian and Gieseke, Fabian},title={Training Neural Networks End-to-End for Hyperbox-Based Classification},journal={Neurocomputing},pages={127961},year={2024},}
