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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 K-NN algorithm. A fuzzy rule-based initialization procedure differentiates our proposed algorithm from the conventional fuzzy K-NN algorithm. The new initialization procedure allows us to handle the imprecise inputs (neighborhood density and distance) through the natural framework of fuzzy logic system. Unlike conventional K-NN algorithms, the ability to fine tune the membership functions can lead to a highly versatile decision boundary. Thus, the new algorithm can be specifically tuned for different problems to …