Fuzzy classifier with bayes rule consequent

Authors:
Do Wan Kim;Jin Bae Park;Young Hoon Joo
Affiliations:
Yonsei University, Seoul, Korea;Yonsei University, Seoul, Korea;Kunsan National University, Kunsan, Chunbuk, Korea
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

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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.