KNN (k-nearest-neighbor) has been widely used as an effective classification model. In this paper, we summarize three main shortcomings confronting KNN and single out three main methods for overcoming its three shortcomings. Keeping to these methods, we try our best to survey some improved algorithms and experimentally tested their effectiveness. Besides, we discuss some directions for future study on KNN.
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Jiang et al. (2007) studied this question.