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.

翻译：超盒分类被视为一种有前景的技术，其中对数据的决策被表示为一系列正交的多维矩形（即超盒），这些超盒通常具有可解释性和人类可读性。然而，现有方法已无法高效处理当今许多应用领域面临的数据量日益增长的问题。为解决这一不足，我们提出了一种新颖的、完全可微的神经网络框架，用于实现超盒分类。与以往工作不同，我们的超盒模型能够以端到端的方式进行高效训练，从而显著缩短训练时间并获得更优的分类结果。