An Empirical Evaluation of Rule Extraction from Recurrent Neural Networks

Rule extraction from black-box models is critical in domains that require model validation before implementation, as can be the case in credit scoring and medical diagnosis. Though already a challenging problem in statistical learning in general, the difficulty is even greater when highly non-linear...

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Bibliographic Details
Published in:arXiv.org 2018-11
Main Authors: Wang, Qinglong, Zhang, Kaixuan, Ororbia, Alexander G, Xing, Xinyu, Liu, Xue, C Lee Giles
Format: Article
Language:English
Subjects:
Domains
Grammars
Neural networks
Recurrent neural networks
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Summary:Rule extraction from black-box models is critical in domains that require model validation before implementation, as can be the case in credit scoring and medical diagnosis. Though already a challenging problem in statistical learning in general, the difficulty is even greater when highly non-linear, recursive models, such as recurrent neural networks (RNNs), are fit to data. Here, we study the extraction of rules from second-order recurrent neural networks trained to recognize the Tomita grammars. We show that production rules can be stably extracted from trained RNNs and that in certain cases the rules outperform the trained RNNs.
ISSN:2331-8422