Evaluating the Ambiguities Between Two Classes via Euclidean Distance

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题名:	Evaluating the Ambiguities Between Two Classes via Euclidean Distance
作者:	王經篤;Wang, Jing-Doo;劉湘川;Liu, Hsiang-Chuan
贡献者:	生物與醫學資訊學系
关键词:	classification;taxonomy;class structure;class ambiguity.
日期:	2009-03
上传时间:	2012-11-23 09:16:07 (UTC+0)
摘要:	"The classification was a supervised learning approach and, therefore, the class structure was specified in advance by domain experts. The goal of this paper is to evaluate the degree of ambiguity between two classes in the existing class structure via Euclidean distance. In this paper, Distinguishable Distance Ratio (DDR) and Class Ambiguity Ratio (CAR) between two classes are proposed to indicate the degree of the ambiguity between classes. The degree of class ambiguity between two classes is expected to be high if the value of DDR is low and the value of CAR is high. The experimental resources for class structure evaluation includes “Iris Plant,” “Wine Recognition” and “Glass Identification,” and the values of DDR and CAR were found to reveal the degree of class ambiguity. This work offers domain expertise an approach to examine the fitness of class structure, if necessary. To our knowledge, we are the first to address the problem of the ambiguity of class structure"
關聯:	Asian Journal of Health and Information Sciences
显示于类别:	[生物資訊與醫學工程學系 ] 期刊論文

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