HyDI: A hybrid Deep Learning-Inductive Logic Programming ensemble for multi-label classification

Simon Flügel; Till Mossakowski

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arXiv · 2609.37740

HyDI: A hybrid Deep Learning-Inductive Logic Programming ensemble for multi-label classification

Simon Flügel·Till Mossakowski

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Abstract

While attaining remarkable results for many applications, Deep Learning models are notoriously difficult to explain. This work introduces HyDI, a hybrid ensemble architecture for hierarchical multi-label classification. It combines a Deep Learning (DL) model with rule-based classifiers generated by Inductive Logic Programming (ILP). For leaf classes of the label hierarchy, the rule-based classifiers replace the DL model, leading to more transparent classification results. HyDI is applied to the Chemical Entities of Biological Interest (ChEBI) ontology, providing ILP-generated rules for 314 classes. For these classes, HyDI can generate global explanations as well as local explanations that combine visual and text-based descriptions.

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BibTeX RIS

Simon Flügel, Till Mossakowski. 2026-09-29. HyDI: A hybrid Deep Learning-Inductive Logic Programming ensemble for multi-label classification. https://arxiv.org/abs/2609.37740

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