文章基本信息

标题：A Survey of Distance Metrics for Nominal Attributes
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作者：Li, Chaoqun ; Li, Hongwei
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2010
卷号：5
期号：11
页码：1262-1269
DOI：10.4304/jsw.5.11.1262-1269
语种：English
出版社：Academy Publisher
摘要：Many distance-related algorithms, such as k-nearest neighbor learning algorithms, locally weighted learning algorithms etc, depend upon a good distance metric to be successful. In this kind of algorithms, a key problem is how to measure the distance between each pair of instances. In this paper, we provide a survey on distance metrics for nominal attributes, including some basic distance metrics and their improvements based on attribute weighting and attribute selection. The experimental results on the whole 36 UCI datasets published on the main web site of Weka platform validate their effectiveness.
关键词：distance metric; attribute weighting; attribute selection; nominal attributes; classification