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Communication Dans Un Congrès Année : 2023

Neural-based classification rule learning for sequential data

Philippe Bonnard
  • Fonction : Auteur
  • PersonId : 1090527
IBM
Remy Kusters
  • Fonction : Auteur
  • PersonId : 1143106
IBM

Résumé

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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Dates et versions

hal-04093337 , version 1 (10-05-2023)

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Marine Collery, Philippe Bonnard, François Fages, Remy Kusters. Neural-based classification rule learning for sequential data. ICLR 2023 - The Eleventh International Conference on Learning Representations, May 2023, Kigali, Rwanda. ⟨10.48550/arXiv.2302.11286⟩. ⟨hal-04093337⟩
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