Knowledge based descriptive neural networks

Authors:
J. T. Yao
Affiliations:
Department of Computer Science, University or Regina, Regina, Saskachewan, Canada
Venue:
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Year:
2003

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Abstract

This paper presents a study of knowledge based descriptive neural networks (DNN). DNN is a neural network that incorporates rules extracted from trained neural networks. One of the major drawbacks of neural network models is that they could not explain what they have done. Extracting rules from trained neural networks is one of the solutions. However, how to effectively use extracted rules has been paid little attention. This paper addresses issues of effective ways of using these extracted rules. With the introduction of DNN, we not only keep the good feature of nonlinearity in neural networks but also have explanation of underlying reasoning mechanisms, for instance, how prediction is made.