Research on an Adaptive k-Nearest Neighbor Algorithm
一种自适应k-最近邻算法的研究*

An improved adaptive k-nearest neighbor algorithm is brought forward because the traditional k-nearest neighbor algorithm has certain limitation that its searching speed is slow. The approach searches a super ball for the k-nearest neighbors, which takes the testing sample as its center. According to the radius growth of the super ball and the numbers of samples in the super ball,a BP model will be built to approximate the changing function of the radius.Then the BP model is used to guide the radius growth. The approach can effectively reduce the searching range and decrease the time of the super ball growth, which is very fit for sparse datum set.