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Journal of Applied Sciences
  Year: 2011 | Volume: 11 | Issue: 15 | Page No.: 2855-2860
DOI: 10.3923/jas.2011.2855.2860
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Explaining Results of Artificial Neural Networks

D. Yedjour, H. Yedjour and A. Benyettou

Neural networks are very efficient in solving various problems but they have no ability of explaining their answers and presenting gathered knowledge in a comprehensible way. Two main approaches are used, namely the pedagogical one that treats a network as a black box and the local one that examines its structure. Because searching rules is similar to NP-hard problem it justifies an application of evolutionary algorithm to the rule extraction. Pedagogical approaches such as GA are insensitive to the number of units of neural networks as they see them as "black boxes" interested only their inputs and their outputs. In the study we describe new rule extraction method based on evolutionary algorithm called GenRGA. It uses logical rules and is composed of three (03) main parts: genetic module, neural networks module and rules simplification module. GenRGA is tested in experimental studies using different benchmark data sets from UCI repository. Comparisons with other methods show that the extracted rules are accurate and highly comprehensible.
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How to cite this article:

D. Yedjour, H. Yedjour and A. Benyettou, 2011. Explaining Results of Artificial Neural Networks. Journal of Applied Sciences, 11: 2855-2860.

DOI: 10.3923/jas.2011.2855.2860






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