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題名 Time-constrained Cost Sensitive Decision Tree Induction
作者 唐揆
Chen, Y.L.;Wu, C.C.;Tang, Kwei
貢獻者 企管系
關鍵詞 Data mining; Decision analysis; Cost-sensitive learning; Classification; Decision tree
日期 2016-08
上傳時間 25-Aug-2016 14:11:51 (UTC+8)
摘要 A cost-sensitive decision tree is induced for the purpose of building a decision tree from training data that minimizes the sum of the misclassification cost and test cost. Although this problem has been investigated extensively, no previous study has specifically focused on how the decision tree can be induced if the classification task must be completed within a limited time. Accordingly, we developed an algorithm to generate a time-constrained minimal-cost tree. The main idea behind the algorithm is to select the attribute that brings the maximal benefit when time is sufficient, and to select the most time-efficient attribute (i.e., the attribute that provides maximal benefit per unit time) when time is limited. Our experimental results show that the performance of this algorithm is highly satisfactory under various time constraints across distinct datasets.
關聯 Information Sciences, 354, 140-152
資料類型 article
DOI http://dx.doi.org/10.1016/j.ins.2016.03.022
dc.contributor 企管系
dc.creator (作者) 唐揆zh_TW
dc.creator (作者) Chen, Y.L.;Wu, C.C.;Tang, Kwei
dc.date (日期) 2016-08
dc.date.accessioned 25-Aug-2016 14:11:51 (UTC+8)-
dc.date.available 25-Aug-2016 14:11:51 (UTC+8)-
dc.date.issued (上傳時間) 25-Aug-2016 14:11:51 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/100736-
dc.description.abstract (摘要) A cost-sensitive decision tree is induced for the purpose of building a decision tree from training data that minimizes the sum of the misclassification cost and test cost. Although this problem has been investigated extensively, no previous study has specifically focused on how the decision tree can be induced if the classification task must be completed within a limited time. Accordingly, we developed an algorithm to generate a time-constrained minimal-cost tree. The main idea behind the algorithm is to select the attribute that brings the maximal benefit when time is sufficient, and to select the most time-efficient attribute (i.e., the attribute that provides maximal benefit per unit time) when time is limited. Our experimental results show that the performance of this algorithm is highly satisfactory under various time constraints across distinct datasets.
dc.format.extent 680668 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Information Sciences, 354, 140-152
dc.subject (關鍵詞) Data mining; Decision analysis; Cost-sensitive learning; Classification; Decision tree
dc.title (題名) Time-constrained Cost Sensitive Decision Tree Induction
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1016/j.ins.2016.03.022
dc.doi.uri (DOI) http://dx.doi.org/10.1016/j.ins.2016.03.022