Title: Neural-based classification rule learning for sequential data. Abstract: 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.