Neural-based classification rule learning for sequential data

TL;DR

This paper introduces a novel differentiable neural method for discovering interpretable local and global patterns in sequential data, enhancing rule-based binary classification with dynamic sparsity and interpretability.

Contribution

It presents a fully interpretable convolutional neural network with a neural filter and dynamic sparsity for rule discovery in sequential data, combining pattern learning with rule formation.

Findings

Effective on synthetic datasets

Successful application to peptides dataset

Enhances interpretability of sequence classification

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.