关键词: 协同训练, 半监督聚类, 加权[K]最近邻, 视图

Abstract: In the process of co-training iteration, the lack of useful information implied by the selection of unmarked samples and the inconsistency of multiple classifier markers will lead to the unmarked samples of error marks. Aiming at the above questions, this paper proposes a co-training method combined with a semi-supervised clustering and the weighted [K]-nearest neighbor. In the process of each iteration, the method first carries out a semi-supervised clustering on the training set, chooses the unmarked samples with high membership degree to the naive Bayes classification, and then uses the weighted [K]-nearest neighbor algorithm to reclassify the inconsistent unmarked samples classified by multiple classifier. Using a semi-supervised clustering can choose the better performance data of the space structure of samples, and using the weighted [K]-nearest neighbor algorithm to mark the inconsistent unmarked samples can solve the problem of classification accuracy degradation caused by inconsistent marking. The comparison experiment on UCI dataset verifies the validity of the algorithm.

Key words: co-training, semi-supervised clustering, weighted [K]-nearest neighbor, view