Semi-supervised k-nearest neighbor classification method
半监督k近邻分类方法

The category information of the k-nearest neighbor labeled samples is used, but the contribution of the test samples is omitted in the weighted k-nearest neighbor method, which often lead to misclassifications. Aimed at the problem, a semi-supervised k-nearest neighbor method is proposed in this paper. The method can classify sequential samples and non-sequential samples better than the k-nearest neighbor method. In the decision process of classification, the information of c-nearest neighbor samples in the test set is used. So, classification accuracy is improved. The recognition accuracy of the method is 5.95% higher for sequential images in Cohn-Kanade face database, and 7.89% higher for non-sequential images in Cohn-Kanade face database than it of weighted k-nearest neighbor method. The experiment shows that the method performs fast and has high classification accuracy.