NDLI: Literal and ProRulext: algorithms for rule extraction of ANNs

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Literal and ProRulext: algorithms for rule extraction of ANNs

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Fourth International Conference on Hybrid Intelligent Systems (HIS'04)

Literal and ProRulext: algorithms for rule extraction of ANNs

Content Provider	IEEE Xplore Digital Library
Author	Campos, P.G. Ludermir, T.B.
Copyright Year	2005
Description	Author affiliation: Center of Comput. Sci., Pernambuco Fed. Univ., Brazil (Campos, P.G.; Ludermir, T.B.)
Abstract	Artificial neural networks (ANN) present excellent capacity for generalization. Besides, they are applied to the most diverse human knowledge domains. However, since they represent knowledge in its topology, weight values and bias, explaining clearly how an ANN has obtained its outputs is not a trivial task for human experts. Usually such deficiency can be minimized through the "if/then" rule extraction from the trained network. Thus, this work presents two algorithms for the propositional rule extraction from trained ANNs: literal and ProRulext. Among other advantages, these methods can be applied to trained networks for pattern classification and time series forecast, obtaining rules that are compact, comprehensible and faithful to the networks from which they have been extracted, also at a lower computational cost compared to NeuroLinear.
File Size	210214
File Format	PDF
ISBN	0769524575
DOI	10.1109/ICHIS.2005.69
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-11-06
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Network topology Supervised learning Humans Pattern classification Artificial neural networks Evolutionary computation Multilayer perceptrons Computational efficiency Electronic mail
Content Type	Text
Resource Type	Article

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