Abstract:  To solve the problem of the low prediction efficiency of traditional K-NN classification, this paper presents a speeding K-Nearest Neighbor (K-NN) classification method based on testing sample label (KNN_TSL). Firstly, a certain number of testing samples is obtained by traditional K-NN classification method. Then for the samples to be entered latterly, the distance between the labeled samples and the testing sample is calculated. If the distance is less than a given threshold, the new entry sample is assigned the same class label. Otherwise, the K-NN classification is performed. By this method, most last easily classified samples can be decided only by considering the relationship of it with the labeled testing samples, and only a small number of samples is reclassified. Because the labeled samples contain some information of class, this method can greatly improve the classification prediction efficiency and ensure the generalization performance. The experiment result demonstrates that the proposed KNN_TSL model can obtain the high learning efficiency and testing accuracy simultaneously.

Key words: K-NN classification, testing sample label, KNN_TSL algorithm