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Interpretation of Artificial Neural Networks: Mapping knowledge-based Neural Networks into Rules. Advances in Neural Information Processing Systems, pp. 977-984, Denver, CO. Morgan Kaufmann.
Data.

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Abstract:

We propose and empirically evaluate a method for the extraction of expert-comprehensible rules from trained neural networks. Our method operates in the context of a three-step process for learning that uses rule-based domain knowledge in combination with neural networks. Empirical tests using real-worlds problems from molecular biology show that the rules our method extracts from trained neural networks: closely reproduce the accuracy of the network from which they came, are superior to the rules derived by a learning system that directly refines symbolic rules, and are expert-comprehensible.

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