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arXiv · 2302.11286

Neural-based classification rule learning for sequential data

Marine Collery·Philippe Bonnard·François Fages·Remy Kusters
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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.

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Marine Collery, Philippe Bonnard, François Fages, Remy Kusters. 2023-02-22. Neural-based classification rule learning for sequential data. https://arxiv.org/abs/2302.11286

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