Rule Learning by Modularity
It addresses the need for explainable AI in industrial applications like insurance, though it appears incremental as it combines existing methods.
The paper tackles the problem of classifying large datasets efficiently and scalably while maintaining explainability, by combining stochastic machine learning with traditional rule learning, and demonstrates results on MNIST, Fashion-MNIST, IMDB, and a novel case study on dental bills.
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.