Communications of the ACM
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Explanation and artificial neural networks
International Journal of Man-Machine Studies
C4.5: programs for machine learning
C4.5: programs for machine learning
Knowledge-based artificial neural networks
Artificial Intelligence
The Foundations of Artificial Intelligence: A SourceBook
The Foundations of Artificial Intelligence: A SourceBook
Machine Learning
Extracting Provably Correct Rules from Artificial Neural Networks
Extracting Provably Correct Rules from Artificial Neural Networks
Extracting comprehensible models from trained neural networks
Extracting comprehensible models from trained neural networks
An investigation of TREPAN utilising a continuous oracle model
International Journal of Data Analysis Techniques and Strategies
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This paper addresses the extraction of symbolic knowledge from trained artificial neural networks. Specifically, for that purpose the so-called pedagogical approach is incorporated, where the trained network is used as an oracle when inducing the symbolic description. We present an essential extension of the TREPAN algorithm proposed originally by Craven and Shavlik [4][5]. The crucial modification concerns the way of generating artificial training instances. The paper ends with an empirical verification of the proposed method on popular machine learning benchmarks and comparison with the original TREPAN.