Abstract
The technology of artificial neural networks has been proven to be well-suited for the mining of useful information from vast quantities of data. Most work focuses on the pursuit of accurate results but neglects the reasoning process. This “black-box” feature is the main drawback of artificial neural network mining models. However, the practicability of many mining tasks relies not only on accuracy, reliability and tolerance but also on the explanatory ability. Rule extraction is a technique for extracting symbolic rules from artificial neural networks and can therefore transfer the features of artificial neural networks from “black-box” into “white-box”. This paper proposes a novel approach in which knowledge is extracted, in the forms of symbolic rules, from one-dimensional self-organizing maps. Three data sets are used in this paper. The experimental results demonstrate that this proposed approach not only equips the self-organizing map with an explanatory ability based on symbolic rules, but also provides a robust generalized ability for unseen data sets.
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Hung, C. (2009). Knowledge-Based Rule Extraction from Self-Organizing Maps. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_17
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DOI: https://doi.org/10.1007/978-3-642-03040-6_17
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