计算机工程与应用2019，Vol.55Issue(22)：114-118,5.DOI:10.3778/j.issn.1002-8331.1807-0159
结合半监督聚类和加权KNN的协同训练方法
Co-Training Method Combined with Semi-Supervised Clustering and Weighted K-Nearest Neighbor
摘要
Abstract
In the process of co-training iteration, the lack of useful information implied by the selection of unmarked sam-ples 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 train-ing 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 classifica-tion accuracy degradation caused by inconsistent marking. The comparison experiment on UCI dataset verifies the validity of the algorithm.关键词
协同训练/半监督聚类/加权K最近邻/视图Key words
co-training/semi-supervised clustering/weighted K-nearest neighbor/view分类
信息技术与安全科学引用本文复制引用
龚彦鹭,吕佳..结合半监督聚类和加权KNN的协同训练方法[J].计算机工程与应用,2019,55(22):114-118,5.基金项目
重庆市自然科学基金(No.cstc2014jcyjA40011) （No.cstc2014jcyjA40011）
重庆市教委科技项目(No.KJ1400513) （No.KJ1400513）
重庆师范大学科研项目(No.YKC17001). （No.YKC17001）
