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題名 A Dynamic Discretization Approach for Constructing Decision Trees with a Continuous Label
作者 唐揆
Tang, Kwei
貢獻者 企管系
關鍵詞 Decision trees; data mining; classification
日期 2009.11
上傳時間 16-十月-2014 17:52:04 (UTC+8)
摘要 In traditional decision (classification) tree algorithms, the label is assumed to be a categorical (class) variable. When the label is a continuous variable in the data, two possible approaches based on existing decision tree algorithms can be used to handle the situations. The first uses a data discretization method in the preprocessing stage to convert the continuous label into a class label defined by a finite set of nonoverlapping intervals and then applies a decision tree algorithm. The second simply applies a regression tree algorithm, using the continuous label directly. These approaches have their own drawbacks. We propose an algorithm that dynamically discretizes the continuous label at each node during the tree induction process. Extensive experiments show that the proposed method outperforms the preprocessing approach, the regression tree approach, and several nontree-based algorithms.
關聯 IEEE Transactions on Knowledge and Data Engineering, 21(11), 1505-1514
資料類型 article
DOI http://dx.doi.org/10.1109/TKDE.2009.24
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) 2009.11en_US
dc.date.accessioned 16-十月-2014 17:52:04 (UTC+8)-
dc.date.available 16-十月-2014 17:52:04 (UTC+8)-
dc.date.issued (上傳時間) 16-十月-2014 17:52:04 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/70626-
dc.description.abstract (摘要) In traditional decision (classification) tree algorithms, the label is assumed to be a categorical (class) variable. When the label is a continuous variable in the data, two possible approaches based on existing decision tree algorithms can be used to handle the situations. The first uses a data discretization method in the preprocessing stage to convert the continuous label into a class label defined by a finite set of nonoverlapping intervals and then applies a decision tree algorithm. The second simply applies a regression tree algorithm, using the continuous label directly. These approaches have their own drawbacks. We propose an algorithm that dynamically discretizes the continuous label at each node during the tree induction process. Extensive experiments show that the proposed method outperforms the preprocessing approach, the regression tree approach, and several nontree-based algorithms.en_US
dc.format.extent 2874086 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.relation (關聯) IEEE Transactions on Knowledge and Data Engineering, 21(11), 1505-1514en_US
dc.subject (關鍵詞) Decision trees; data mining; classificationen_US
dc.title (題名) A Dynamic Discretization Approach for Constructing Decision Trees with a Continuous Labelen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1109/TKDE.2009.24en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1109/TKDE.2009.24 en_US