西华大学学报（自然科学版）2017，Vol.36Issue(4)：93-97,5.DOI:10.3969/j.issn.1673-159X.2017.04.015
一种改进的K-近邻分类法
An Improved K-nearest Neighbor Cassification Method
摘要
Abstract
This paper introduces the basic ideas and research status of the existing K-nearest neighbor method,and improve the low classification accuracy when all kinds of data sets are distributed unbalanced.In the improved K-nearest neighbor method,the class representation and sample representation are introduced,so that the nearest neighbor samples,which were selected by K-nearest neighbor classification in the similarity calculation,were more representative of its class,thus reducing the false positive rate.The validity of the improved method is proved by experiments.关键词
K-近邻分类法/不平衡样本/有效性/类代表度/样本代表度Key words
K-nearest neighbor classification method/unbalanced sample/effectiveness/class representation/sample representative degree分类
信息技术与安全科学引用本文复制引用
苏佩娟,刘赪,牟建波,王丽梅..一种改进的K-近邻分类法[J].西华大学学报（自然科学版）,2017,36(4):93-97,5.基金项目
西南交通大学随机数学及其应用(2682014ZT29). （2682014ZT29）
