关键词: 集成自训练, 近邻密度, 半监督, K近邻（KNN）

Abstract: Integrated self-training algorithm is apt to locally overfit during iteration when it is used to randomly initialize labeled samples, which leads to poor generalization to the original sample space structure. Additionally, integrated self-training algorithm with WKNN classifier, which is adopted to edit data, doesn’t take into account the unlabeled samples’ effect on class labels of test samples. Thus, an integrated self-training algorithm based on nearest neighbor density and semi-supervised KNN is proposed in this paper. The algorithm uses the nearest neighbor density to select the initially labeled samples to avoid choosing K nearest neighbor samples around labeled samples as labeled sample candidates, so the distribution of the selective samples will be decentralized and it can better reflect the sample space structure. At the same time, in order to improve the performance of data clips, semi-supervised KNN is used into the algorithm instead of WKNN. It chooses the unlabeled samples with the highest density as the labeled samples so that it can make full use of unlabeled samples. The effectiveness of the presented algorithm is verified by comparative experiments on UCI datasets.

Key words: integrated self-training, nearest neighbor density, semi-supervised, K Nearest Neighbor（KNN）