Study on the Improvement of K-Nearest-Neighbor Algorithm

As one of the instance based learning method, the K-nearest-neighbor (KNN) algorithm has been widely used in many fields. This paper accomplishes the improvements on the two aspects. First, aiming to improve the efficiency of classifying, we move some computations occurring at classifying period to the training period, which leads to the great descent of computational cost. Second, to improve the accuracy of classifying, we take into account of the contribution of different attributes and obtain the optimal attribute weight sets using the quadratic programming method. Finally, this paper gives the validation of the improvements through practical experiment.