Discovering interpretable patterns for classification of sequential data is of key importance for a variety of fields, ranging from genomics to fraud detection or more generally interpretable decision-making. In this paper, we propose a novel differentiable fully interpretable method to discover both local and global patterns (i.e. catching a relative or absolute temporal dependency) for rule-based binary classification. It consists of a convolutional binary neural network with an interpretable neural filter and a training strategy based on dynamically-enforced sparsity. We demonstrate the validity and usefulness of the approach on synthetic datasets and on an open-source peptides dataset. Key to this end-to-end differentiable method is that the expressive patterns used in the rules are learned alongside the rules themselves.

翻译：为多种领域（从基因组学到欺诈检测，或更广义的可解释决策制定）发现用于序列数据分类的可解释模式至关重要。本文提出一种新颖的、完全可微且可解释的方法，用于挖掘基于规则的二分类中的局部与全局模式（即捕获相对或绝对时间依赖性）。该方法包含一个带有可解释神经滤波器的卷积二值神经网络，并采用基于动态强制稀疏性的训练策略。我们通过合成数据集和开源多肽数据集验证了该方法的有效性与实用性。该端到端可微方法的关键在于，规则中使用的表达性模式与规则本身能够同步学习。