NDLI: Generating rules from trained network using fast pruning

Content Provider	IEEE Xplore Digital Library
Author	Setiono, R. Wee Kheng Leow
Copyright Year	1999
Description	Author affiliation: Sch. of Comput., Nat. Univ. of Singapore, Singapore (Setiono, R.)
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 re-training. Given a fully connected trained feedforward network, FERNN first identifies the relevant hidden units by computing their information gains. Next, it identifies relevant connections from the input units to the relevant hidden units by checking the magnitudes of their weights. Finally, FERNN generates rules based on the relevant hidden units and weights. Our experimental results show that the size and accuracy of the tree generated are comparable to those extracted by another method which prunes and retrains the network.
Starting Page	4095
Ending Page	4098
File Size	414315
Page Count	4
File Format	PDF
ISBN	0780355296
ISSN	10987576
DOI	10.1109/IJCNN.1999.830817
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-07-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biological neural networks Computer networks Neural networks Decision trees Costs Feedforward neural networks Entropy Classification tree analysis Message-oriented middleware Artificial neural networks
Content Type	Text
Resource Type	Article

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