Rule Learning by Modularity
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Abstract (arXiv)
In this paper, we present a modular methodology that combines state-of-the-art methods in (stochastic) machine learning with traditional methods in rule learning to provide efficient and scalable algorithms for the classification of vast data sets, while remaining explainable. Apart from evaluating our approach on the common large scale data sets MNIST, Fashion-MNIST and IMDB, we present novel results on explainable classifications of dental bills. The latter case study stems from an industrial collaboration with Allianz Private Krankenversicherungs-Aktiengesellschaft which is an insurance company offering diverse services in Germany.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 22 of its 59 references that are in the map. First posted on arXiv 23 Dec 2022.
Builds on
22 of 59 references.
References · 22
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10 more
- 2021Inductive logic programming at 30cited by 46
- 2019The Convolutional Tsetlin Machinecited by 37
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- 2020Rule Extraction from Binary Neural Networks with Convolutional Rules for Model Validationcited by 3
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