考虑局部均值和类全局信息的快速近邻原型选择算法

被引：9

作者：

李娟 ^{[1
,2
]}

王宇平 ^{[1
]}

机构：

[1] 西安电子科技大学计算机学院

[2] 陕西师范大学远程教育学院

来源：

自动化学报 | 2014年 / 40卷 / 06期

关键词：

数据分类; 原型选择; 局部均值; 类全局信息; 自适应学习;

D O I：

暂无

中图分类号：

TP301.6 [算法理论];

学科分类号：

081202 ;

摘要：

压缩近邻法是一种简单的非参数原型选择算法,其原型选取易受样本读取序列、异常样本等干扰.为克服上述问题,提出了一个基于局部均值与类全局信息的近邻原型选择方法.该方法既在原型选取过程中,充分利用了待学习样本在原型集中k个同异类近邻局部均值和类全局信息的知识,又设定原型集更新策略实现对原型集的动态更新.该方法不仅能较好克服读取序列、异常样本对原型选取的影响,降低了原型集规模,而且在保持高分类精度的同时,实现了对数据集的高压缩效应.图像识别及UCI(University of California Irvine)基准数据集实验结果表明,所提出算法集具有较比较算法更有效的分类性能.

引用

页码：1116 / 1125

页数：10

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