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Accuracy of the well-known k-nearest neighbor (kNN) classifier heavily depends on the choice of k. The problem of estimating a suitable k for any test point becomes difficult due to several factors like the local distribution of training points around that test point, presence of outliers in the dataset, and, dimensionality of the feature space. In this paper, we propose a dynamic k estimation algorithm...
Accuracy of the well-known kNN classifier depends significantly on the suitable choice of k. In this paper, we propose an improved kNN algorithm with a novel non-parametric test point specific k estimation strategy. To estimate k for any test point, we first construct a hyper sphere around it to capture the local distribution of the surrounding training points. Class hubness information is then used...
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