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考虑局部均值和类全局信息的快速近邻原型选择算法

DOI: 10.3724/SP.J.1004.2014.01116, PP. 1116-1125

Keywords: 数据分类,原型选择,局部均值,类全局信息,自适应学习

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Abstract:

?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(UniversityofCaliforniaIrvine)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.

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