An empirical evaluation of rule extraction from recurrent neural networks

Qinglong Wang, Kaixuan Zhang, Alexander G. Ororbia, Xinyu Xing, Xue Liu, C. Lee Giles

Research output: Contribution to journalLetterpeer-review

20 Scopus citations

Abstract

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 nonlinear, recursive models, such as recurrent neural networks (RNNs), are fit to data. Here, we study the extraction of rules from second-order RNNs 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.

Original languageEnglish (US)
Pages (from-to)2568-2591
Number of pages24
JournalNeural Computation
Volume30
Issue number9
DOIs
StatePublished - Sep 1 2018
Externally publishedYes

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience

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