Abstract
Fuzzy ARTMAP is capable of incrementally learning interpretable rules. To remove unused or inaccurate rules, a rule pruning method has been proposed in the literature. This paper addresses its limitations when incremental learning is used, and modifies it so that it does not need to store previously learnt samples. Experiments show a better performance, especially in concept drift problems.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Andrés-Andrés, A., Gómez-Sánchez, E., Bote-Lorenzo, M.L. (2005). Incremental Rule Pruning for Fuzzy ARTMAP Neural Network. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_104
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DOI: https://doi.org/10.1007/11550907_104
Publisher Name: Springer, Berlin, Heidelberg
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