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11

NPL
1998

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Extraction of Logical Rules from Neural Networks

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Extraction of Logical Rules from Neural Networks

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A new architecture and method for feature selection and extraction of logical rules from neural networks trained with backpropagation algorithm is presented. The network consists of nodes that discover linguistic features and nodes that discover logical rules. Most weights are constrained to ±1 or zero values. The relevant input features are automatically generated and selected by the network. Rules are generated consecutively, from the most general, covering many training examples, to the most speciﬁc, covering exceptions only. Automatic weight pruning ensures that a minimal number of logical rules is found. Results for the Iris classiﬁcation problem illustrate the eﬃciency of this method. Keywords Neural networks, MLP, backpropagation, logical rule extraction, feature selection, Iris dataset.

Wlodzislaw Duch, Rafal Adamczak, Krzysztof Grabcze

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Added	22 Dec 2010
Updated	22 Dec 2010
Type	Journal
Year	1998
Where	NPL
Authors	Wlodzislaw Duch, Rafal Adamczak, Krzysztof Grabczewski

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