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Abstract: Before symbolic rules are extracted from a trained neural network, the network is usually pruned so as to obtain more concise rules. Typical pruning algorithms require retraining the network which incurs additional cost. This paper presents FERNN, a fast method for extracting rules from trained neural networks without network retraining. Given a fully connected trained feedforward network with a single hidden layer, FERNN first identifies the relevant hidden units by computing their information ... (Update)
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BibTeX entry: (Update)
R. Setiono and W.K. Leow, \FERNN: An algorithm for fast extraction of rules from neural networks," Applied Intelligence, vol. 12, no. 1/2, pp. 15-25, 2000. http://citeseer.ist.psu.edu/setiono00fernn.html More
@article{ setiono00fernn,
author = "Rudy Setiono and Wee Kheng Leow",
title = "{FERNN}: An Algorithm for Fast Extraction of Rules from Neural Networks",
journal = "Applied Intelligence",
volume = "12",
number = "1-2",
pages = "15-25",
year = "2000",
url = "citeseer.ist.psu.edu/setiono00fernn.html" }
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