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The performances of conventional crisp and fuzzy K-nearest neighbor (K-NN) algorithms trained using finite samples tends to be poor . With ldquoholesrdquo in the training data, it is unlikely that the decision area formed can actually represent the underlying data distribution. There is a need to capture more useful information from the limited training samples, therefore we propose a new fuzzy rule-based...
This paper renders a fuzzy nearest neighbor classifier with data pruning to reduce the number of stored prototypes to minimize memory and computational time requirements. The incorporation of fuzzy set theory into nearest neighbor classification makes the decision process more flexible and adaptable to noise in the data. We have also embodied an efficient approach for nearest neighbor search in our...
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