End-to-End Neural Network Training for Hyperbox-Based Classification
This work addresses the scalability problem for practitioners in domains requiring interpretable classification, though it is incremental as it builds on existing hyperbox techniques.
The authors tackled the inefficiency of existing hyperbox-based classification methods with large datasets by introducing a fully differentiable neural network framework, achieving significantly reduced training times and superior classification results.
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.