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    政大機構典藏 > 商學院 > 企業管理學系 > 期刊論文 >  Item 140.119/70630
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/70630

    Title: Constructing a decision tree from data with Hierarchical Class Labels
    Authors: 唐揆
    Tang, Kwei
    Contributors: 企管系
    Keywords: Classification;Decision tree;Hierarchical class label
    Date: 2009.04
    Issue Date: 2014-10-16 17:52:45 (UTC+8)
    Abstract: Most decision tree classifiers are designed to classify the data with categorical or Boolean class labels. Unfortunately, many practical classification problems concern data with class labels that are naturally organized as a hierarchical structure, such as test scores. In the hierarchy, the ranges in the upper levels are less specific but easier to predict, while the ranges in the lower levels are more specific but harder to predict. To build a decision tree from this kind of data, we must consider how to classify data so that the class label can be as specific as possible while also ensuring the highest possible accuracy of the prediction. To the best of our knowledge, no previous research has considered the induction of decision trees from data with hierarchical class labels. This paper proposes a novel classification algorithm for learning decision tree classifiers from data with hierarchical class labels. Empirical results show that the proposed method is efficient and effective in both prediction accuracy and prediction specificity.
    Relation: Expert Systems with Applications, 36(3), 4838-4847
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1016/j.eswa.2008.05.044
    DOI: 10.1016/j.eswa.2008.05.044
    Appears in Collections:[企業管理學系] 期刊論文

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