论文标题
模块化的规则学习
Rule Learning by Modularity
论文作者
论文摘要
在本文中，我们提出了一种模块化方法，该方法将（随机）机器学习中的最先进方法与规则学习中的传统方法结合在一起，以提供有效且可扩展的算法，以分类庞大的数据集，同时保持可解释。除了评估我们对公共大规模数据集MNIST，Fashion-Mnist和IMDB的方法外，我们还为牙科账单的可解释分类提供了新的结果。后一个案例研究源于与Allianz Private Krankenversicherungs-Aktiengesellsellschaft的工业合作，该公司是一家在德国提供各种服务的保险公司。
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.
