A learning scheme for a fuzzy k-NN rule

Authors:
Adam Jówik
Affiliations:
Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, 00-818 Warsaw, KRN 55, Poland
Venue:
Pattern Recognition Letters
Year:
1983

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Abstract

The performance of a fuzzy k-NN rule depends on the number k and a fuzzy membership-array W[l,m"R], where l and m"R denote the number of classes and the number of elements in the reference set X"R respectively. The proposed learning procedure consists in iterative finding such k and W which minimize the error rate estimate by the leaving 'leaving one out' method.