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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/16707


    Title: Evaluating the Ambiguities Between Two Classes via Euclidean Distance
    Authors: 王經篤;Wang, Jing-Doo;劉湘川;Liu, Hsiang-Chuan
    Contributors: 生物與醫學資訊學系
    Keywords: classification;taxonomy;class structure;class ambiguity.
    Date: 2009-03
    Issue Date: 2012-11-23 17:16:07 (UTC+8)
    Abstract: "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"
    Relation: Asian Journal of Health and Information Sciences
    Appears in Collections:[生物資訊與醫學工程學系 ] 期刊論文

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