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題名 Constructing a decision tree from data with Hierarchical Class Labels
作者 唐揆
Tang, Kwei
貢獻者 企管系
關鍵詞 Classification; Decision tree; Hierarchical class label
日期 2009.04
上傳時間 16-Oct-2014 17:52:45 (UTC+8)
摘要 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.
關聯 Expert Systems with Applications, 36(3), 4838-4847
資料類型 article
DOI http://dx.doi.org/10.1016/j.eswa.2008.05.044
dc.contributor 企管系en_US
dc.creator (作者) 唐揆zh_TW
dc.creator (作者) Tang, Kweien_US
dc.date (日期) 2009.04en_US
dc.date.accessioned 16-Oct-2014 17:52:45 (UTC+8)-
dc.date.available 16-Oct-2014 17:52:45 (UTC+8)-
dc.date.issued (上傳時間) 16-Oct-2014 17:52:45 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/70630-
dc.description.abstract (摘要) 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.en_US
dc.format.extent 861980 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.relation (關聯) Expert Systems with Applications, 36(3), 4838-4847en_US
dc.subject (關鍵詞) Classification; Decision tree; Hierarchical class labelen_US
dc.title (題名) Constructing a decision tree from data with Hierarchical Class Labelsen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1016/j.eswa.2008.05.044en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1016/j.eswa.2008.05.044 en_US