Test Point Specific k Estimation for kNN Classifier

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 hypersphere around it to capture the local distribution of the surrounding training points. Class hubness information is then used as a weight on the hypervolume of the above hyper sphere. Experiments on several UCI benchmark datasets clearly demonstrate the supremacy of our improved kNN algorithm over various existing versions such as i) kNN with fixed values of k (k= 1, 3, 5, 7, [Number of training points]) [1-3], ii) kNN with test point specific k [4], and, iii) kNN with hubness information [5].