计算机应用研究2011，Vol.28Issue(3)：915-917,3.DOI:10.3969/j.issn.1001-3695.2011.03.034
基于小样本集弱学习规则的KNN分类算法
KNN classification algorithm based on rule of weak learning on small sample sets
摘要
Abstract
KNN and its improved algorithms identify the class labels of the unlabeled datasets Du by using the labeled datasets Dl, if the data objects in Dl are very little, and this will influence the accuracy of classification.Improving the accuracy of classification was the goal of KNN classification algorithm based on the rule of weak learning on small sample sets, which learned the label information of objects in Dl based on Dl firstly, and then selected some data objects in Du and labeled them by using the learned label information, finally labeled the objects in Du based on the expanded labeled datasets Dl.The accuracy of the presented method is demonstrated with standard datasets, and obtains a satisfying result.关键词
机器学习/K-最近邻分类/小样本集/标签数据/弱学习规则分类
信息技术与安全科学引用本文复制引用
冷明伟,陈晓云,谭国律..基于小样本集弱学习规则的KNN分类算法[J].计算机应用研究,2011,28(3):915-917,3.基金项目
江西省教育厅青年科学基金资助项目(GJJ09616) （GJJ09616）
江西省教育厅自然科学基金资助项目(GJJ09377) （GJJ09377）
江西省教育厅科学技术研究项目(GJJ11609) （GJJ11609）
