Abstract
This paper presents new pruning and learning methods for the fuzzy rule-based classifier. For the simplicity of the model structure, the unnecessary features for each fuzzy rule are eliminated through the iterative pruning algorithm. The quality of the feature is measured by the proposed correctness method, which is defined as the ratio of the fuzzy values for a set of the feature values on the decision region to one for all feature values. For the improvement of the classification performance, the parameters of the proposed classifier are adjusted by using the gradient descent method so that the misclassified feature vectors are correctly re-categorized. Finally, the fuzzy rule-based classifier is tested on two data sets and is found to demonstrate an excellent performance.
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Kim, D.W., Park, J.B., Joo, Y.H. (2005). Design of Fuzzy Rule-Based Classifier: Pruning and Learning. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539506_54
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DOI: https://doi.org/10.1007/11539506_54
Publisher Name: Springer, Berlin, Heidelberg
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