Symbolic representation of neural networks

Neural networks often surpass decision trees in predicting pattern classifications, but their predictions cannot be explained. This algorithm's symbolic representations make each prediction explicit and understandable. Our approach to understanding a neural network uses symbolic rules to repres...

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Bibliographic Details
Published in:Computer (Long Beach, Calif.) Calif.), 1996-03, Vol.29 (3), p.71-77
Main Authors: Setiono, R., Huan Liu
Format: Article
Language:English
Subjects:
Algorithms
Decision trees
Feedforward systems
Humans
Learning systems
Neural networks
Pattern classification
Predictive maintenance
Rules
Transfer functions
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Summary:Neural networks often surpass decision trees in predicting pattern classifications, but their predictions cannot be explained. This algorithm's symbolic representations make each prediction explicit and understandable. Our approach to understanding a neural network uses symbolic rules to represent the network decision process. The algorithm, NeuroRule, extracts these rules from a neural network. The network can be interpreted by the rules which, in general, preserve network accuracy and explain the prediction process. We based NeuroRule on a standard three layer feed forward network. NeuroRule consists of four phases. First, it builds a weight decay backpropagation network so that weights reflect the importance of the network's connections. Second, it prunes the network to remove irrelevant connections and units while maintaining the network's predictive accuracy. Third, it discretizes the hidden unit activation values by clustering. Finally, it extracts rules from the network with discretized hidden unit activation values.
ISSN:0018-9162
1558-0814
DOI:10.1109/2.485895