language: en

[Appears in Machine Learning: Proceedings of the Tenth International Conference, P. E. Utgoff (editor), Morgan Kaufmann, San Mateo, CA, 1993]

Learning Symbolic Rules Using Artificial Neural Networks

Mark W. Craven and Jude W. Shavlik
Computer Sciences Department University of Wisconsin
1210 West Dayton St. Madison, WI 53706
email: fcraven, shavlikg@cs.wisc.edu

Abstract A distinct advantage of symbolic learning algorithms over artificial neural networks is that typically the concept representations they form are more easily understood by humans. One approach to understanding the representations formed by neural networks is to extract symbolic rules from trained networks. In this paper we describe and investigate an approach for extracting rules from networks that uses (1) the NofM extraction algorithm, and (2) the network training method of soft weight-sharing. Previously, the NofM algorithm had been successfully applied only to knowledge-based neural networks. Our experiments demonstrate that our extracted rules generalize better than rules learned using the C4.5 system. In addition to being accurate, our extracted rules are also reasonably comprehensible.

1 INTRODUCTION