Abstract
Neuro-fuzzy classification systems offer means to obtain fuzzy classification rules by a learning algorithm. It is usually no problem to find a suitable fuzzy classifier by learning from data; however, it can be hard to obtain a classifier that can be interpreted conveniently. There is usually a trade-off between accuracy and readability. In this paper we discuss NEFCLASS – our neuro-fuzzy approach for classification problems – and its most recent JAVA implementation NEFCLASS-J. We show how a comprehensible fuzzy classifier can be obtained by a learning process and how automatic strategies for pruning rules and variables from a trained classifier can enhance its interpretability.
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Nauck, D., Kruse, R. (2000). NEFCLASS-J – A JAVA-Based Soft Computing Tool. In: Azvine, B., Nauck, D.D., Azarmi, N. (eds) Intelligent Systems and Soft Computing. Lecture Notes in Computer Science(), vol 1804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10720181_6
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DOI: https://doi.org/10.1007/10720181_6
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