Mixed fuzzy rule formation

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Mixed fuzzy rule formation

Many fuzzy rule induction algorithms have been proposed during the past decade or so. Most of these algorithms tend to scale badly with large dimensions of the feature space and in addition have trouble dealing with different feature types or noisy data. In this paper, an algorithm is proposed that extracts a set of so called mixed fuzzy rules. These rules can be extracted from feature spaces w...

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In Mixed Fuzzy Rule Formation [Int. J. Approx. Reason. 32 (2003) 67] a method to extract mixed fuzzy rules from data was introduced. The underlying algorithm’s performance is influenced by the choice of fuzzy t-norm and t-conorm, and a heuristic to avoid conflicts between patterns and rules of different classes throughout training. In the following addendum to [Int. J. Approx. Reason. 32 (2003)...

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Corrigendum to: Influence of fuzzy norms and other heuristics on "Mixed fuzzy rule formation" [Int. J. Approx. Reasoning 35 (2004) 195-202]

hereby correct an error in Ref. [2], in which we studied the influence of various parameters that affect the generalization performance of fuzzy models constructed using the mixed fuzzy rule formation method [1]. On page 196, the last equation that computes the normalized loss in volume V

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ژورنال

عنوان ژورنال: International Journal of Approximate Reasoning

سال: 2003

ISSN: 0888-613X

DOI: 10.1016/s0888-613x(02)00077-4