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自适应边界逼近的原型选择算法*

DOI: 10.16451/j.cnki.issn1003-6059.201506012, PP. 568-576

Keywords: 模式分类,原型选择,边界逼近,最近边界原型,自适应原型学习

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Abstract:

针对传统原型选择算法易受样本读取序列、异常样本等干扰的缺陷,通过分析原型算法学习规则,借鉴最近特征线法思想,改进传统原型算法,提出一种自适应边界逼近的原型选择算法.该算法在原型学习过程中改进压缩近邻法的同类近邻吸收策略,保留更优于当前最近边界原型的同类样本,同时建立原型更新准则,并运用该准则实现原型集的周期性动态更新.该算法不仅克服读取序列、异常样本对原型选取的影响,而且降低原型集规模.最后通过人工数据和UCI基准数据集验证文中算法.实验表明,文中算法选择的原型集比其他算法产生的原型集更能体现数据集的分布特征,平均压缩率有所提高,且分类精度与运行时间优于其他算法.

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