计算机工程2011，Vol.37Issue(9)：198-200,3.DOI:10.3969/j.issn.1000-3428.2011.09.069
基于k-最近邻图的小样本KNN分类算法
KNN Classification Algorithm Based on k-Nearest Neighbor Graph for Small Sample
摘要
Abstract
A KNN classification algorithm based on k-nearest neighbor graph for small sample sets is presented to improve the classification accuracy, which partitions the k-nearest neighbor graph into clusters with high similarity, labels the unlabel data of each cluster with the label of the label data in the same cluster, and deletes the noise data.The sample set is expended by this method.The algorithm use the expended sample set to label the unlabel data.The presented algorithm is demonstrated with standard datasets, and the experimental results show the algorithm can enhance the accuracy of classification, reduce the influence of the value of k, and achieve a satisfying result.关键词
KNN算法/k-最近邻图/小样本/图划分/分类算法Key words
KNN algorithm/ k-nearest neighbor graph/ small sample/ graph partitioning/ classification algorithm分类
信息技术与安全科学引用本文复制引用
刘应东,牛惠民..基于k-最近邻图的小样本KNN分类算法[J].计算机工程,2011,37(9):198-200,3.基金项目
甘肃省自然科学研究规划基金资助项(1010RJZA069) （1010RJZA069）
