This paper presents a comparative study of the performance of standard K-nearest neighbour classifier and a fuzzy K-nearest neighbour classifier on a range of data sets. The data sets were artificially generated and the results were compared to those produced by a fuzzy support vector machine classifier (a combination of fuzzy classifiers and kernel machines). The results demonstrated that the fuzzy K-nearest neighbour model has consistently performed better than the K-nearest neighbour model for all data sets. This study shows that the fuzzy K-nearest neighbour model is a valid alternative to the standard K-nearest neighbour model. The fuzzy K-nearest neighbour model has also performed consistently better than the fuzzy support vector machine model, which is a combination of fuzzy classifiers and kernel machines. The results suggest that the fuzzy K-nearest neighbour model outperformed the fuzzy support vector machine model in a wide range of data sets. The fuzzy K-nearest neighbour is therefore a viable alternative to the support vector machine, and may provide a more intuitive and satisfying interpretation to the learning process for many classification problems.
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