计算机工程2019，Vol.45Issue(2)：167-172,6.DOI:10.19678/j.issn.1000-3428.0049901
基于参考点的改进k近邻分类算法
Improvement k-Nearest Neighbor Classification Algorithm Based on Reference Points
摘要
Abstract
The basic k-Nearest Neighbor (kNN) classification algorithm has quadratic time complexity, has a low classification efficiency and has a low classification accuracy. Aiming at this problem, an improvement reference points kNN classification algorithm is proposed. The reference point is selected according to the variance of the point-to-sample distance, and the reference point is given an adaptive weight. Experimental results show that compared with the basic kNN algorithm and kd-tree neighbor algorithm, this algorithm has high classification accuracy and has low time complexity.关键词
k近邻/参考点/自适应权重/方差/分类效率Key words
k-Nearest Neighbor (kNN)/reference points/self-adaptive weight/variance/classification efficiency分类
信息技术与安全科学引用本文复制引用
梁聪,夏书银,陈子忠..基于参考点的改进k近邻分类算法[J].计算机工程,2019,45(2):167-172,6.基金项目
国家重点研发计划 (2016QY01W0200, 2016YFB1000905) （2016QY01W0200, 2016YFB1000905）
重庆市教委科学技术研究项目 (KJ1600426, KJ1600419). （KJ1600426, KJ1600419）
