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A New Approach to the Extraction of ANN Rules and to Their Generalization Capacity Through GP

juanra{at}udc.es, Department of Information and Communications Technologies, University of A Coruña, Faculta de Informática, Campus Elviña s/n, 15192 A Coruña, Spain

Julián Dorado

julian{at}udc.es, Department of Information and Communications Technologies, University of A Coruña, Faculta de Informática, Campus Elviña s/n, 15192 A Coruña, Spain

Alejandro Pazos

apazos{at}udc.es, Department of Information and Communications Technologies, University of A Coruña, Faculta de Informática, Campus Elviña s/n, 15192 A Coruña, Spain

Javier Pereira

javierp{at}udc.es, Department of Information and Communications Technologies, University of A Coruña, Faculta de Informática, Campus Elviña s/n, 15192 A Coruña, Spain

Daniel Rivero

danielrc{at}mail2.udc.es, Department of Information and Communications Technologies, University of A Coruña, Faculta de Informática, Campus Elviña s/n, 15192 A Coruña, Spain

Various techniques for the extraction of ANN rules have been used, but most of them have focused on certain types of networks and their training. There are very few methods that deal with ANN rule extraction as systems that are independent of their architecture, training, and internal distribution of weights, connections, and activation functions. This article proposes a methodology for the extraction of ANN rules, regardless of their architecture, and based on genetic programming. The strategy is based on the previous algorithm and aims at achieving the generalization capacity that is characteristic of ANNs by means of symbolic rules that are understandable to human beings.

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				M. Holena Piecewise-linear neural networks and their relationship to rule extraction from data. Neural Comput., November 1, 2006; 18(11): 2813 - 2853. [Abstract] [Full Text] [PDF]