Summary
A short survey of existing methods of rule extraction from neural networks starts the chapter. Because searching rules is similar to NP-hard problem it justifies an application of evolutionary algorithm to the rule extraction. The survey contains a short description of evolutionary based methods, as well. It creates a background to show own experiences from satisfying applications of evolutionary algorithms to this process. Two methods of rule extraction namely: REX and GEX are presented in details. They represent a global approach to rule extraction, perceiving a neural network by the set of pairs: input pattern and response produced by the neural network. REX uses prepositional fuzzy rules and is composed of two methods REX Michigan and REX Pitt. GEX takes an advantage of classical crisp rules. All details of these methods are described in the chapter. Their efficiency was tested in experimental studies using different benchmark data sets from UCI repository. The comparison to other existing methods was made and is presented in the chapter.
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Markowska-Kaczmar, U. (2008). Evolutionary Approaches to Rule Extraction from Neural Networks. In: Abraham, A., Grosan, C., Pedrycz, W. (eds) Engineering Evolutionary Intelligent Systems. Studies in Computational Intelligence, vol 82. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75396-4_7
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DOI: https://doi.org/10.1007/978-3-540-75396-4_7
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