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Various kinds of k-nearest neighbor (KNN) based classification methods arethe bases of many well-established and high-performance pattern-recognitiontechniques, but both of them are vulnerable to their parameter choice.Essentially, the challenge is to detect the neighborhood of various data sets,while utterly ignorant of the data characteristic. This article introduces anew supervised classification method: the extend natural neighbor (ENaN)method, and shows that it provides a better classification result withoutchoosing the neighborhood parameter artificially. Unlike the original KNN basedmethod which needs a prior k, the ENaNE method predicts different k indifferent stages. Therefore, the ENaNE method is able to learn more fromflexible neighbor information both in training stage and testing stage, andprovide a better classification result.

Extend natural neighbor: a novel classification method with self-adaptive neighborhood parameters in different stages