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.
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Yao, J.T. (2003). Knowledge Based Descriptive Neural Networks. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_72
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DOI: https://doi.org/10.1007/3-540-39205-X_72
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