Abstract
This paper proposes a new fuzzy rule-based classifier equipped with a Bayes rule consequent. The main features of our approach are no requirement on the covariance matrices structure and their avoidance of singularity; the expansion in unimodal densities to multimodal ones; and the fuzzy set analysis for measuring the qualities of features. Two tools are exploited in constructing the proposed classifier: the iterative pruning algorithm for removing the irrelevant features and the gradient descent method for training the related parameters.
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Kim, D.W., Park, J.B., Joo, Y.H. (2005). Fuzzy Classifier with Bayes Rule Consequent. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_154
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DOI: https://doi.org/10.1007/11589990_154
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30462-3
Online ISBN: 978-3-540-31652-7
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