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：基于超盒的分类已被视为一种有前景的技术，其通过一系列正交多维盒（即超盒）来表示数据决策，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所面临的日益增长的数据量。我们通过提出一种新颖的、完全可微分的基于神经网络的超盒分类框架来填补这一空白。与以往工作相比，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。