文章基本信息

标题：Selective Value Difference Metric
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作者：Li, Chaoqun ; Li, Hongwei
期刊名称：Journal of Computers
印刷版ISSN：1796-203X
出版年度：2013
卷号：8
期号：9
页码：2232-2238
DOI：10.4304/jcp.8.9.2232-2238
语种：English
出版社：Academy Publisher
摘要：Value Difference Metric (VDM) is one of the widely used distance functions to define the distance between a pair of instances with nominal attributes only. Many approaches have been proposed to improve the performance of VDM. In this paper, we focus on the attribute selection approach and propose another improved Value Difference Metric. We call it Selective Value Difference Metric (SVDM). In order to learn SVDM, we investigate the attribute independence assumption held by VDM and then single out two effective attribute selection methods for SVDM. The experimental results on 36 UCI benchmark datasets validate the effectiveness of the proposed SVDM.
关键词：Value Difference Metric;Attribute Independence Assumption;Selective Value Difference Metric;Attribute Selection;Naive Bayes.