Interpretation of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks into Rules
Abstract
We propose and empirically evaluate a method for the extraction of expert(cid:173) 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(cid:173) 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.
Cite
Text
Towell and Shavlik. "Interpretation of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks into Rules." Neural Information Processing Systems, 1991.Markdown
[Towell and Shavlik. "Interpretation of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks into Rules." Neural Information Processing Systems, 1991.](https://mlanthology.org/neurips/1991/towell1991neurips-interpretation/)BibTeX
@inproceedings{towell1991neurips-interpretation,
title = {{Interpretation of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks into Rules}},
author = {Towell, Geoffrey and Shavlik, Jude W.},
booktitle = {Neural Information Processing Systems},
year = {1991},
pages = {977-984},
url = {https://mlanthology.org/neurips/1991/towell1991neurips-interpretation/}
}