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題名 A novel decision tree method for structured continuous-label classification
作者 Hu, Hsiao-Wei;Chen, Yen-Liang ;Tang, Kwei
貢獻者 企管系
關鍵詞 Classification algorithms; data mining; decision trees (DTs)
日期 2013.01
上傳時間 16-Oct-2014 17:52:42 (UTC+8)
摘要 Structured continuous-label classification is a variety of classification in which the label is continuous in the data, but the goal is to classify data into classes that are a set of predefined ranges and can be organized in a hierarchy. In the hierarchy, the ranges at the lower levels are more specific and inherently more difficult to predict, whereas the ranges at the upper levels are less specific and inherently easier to predict. Therefore, both prediction specificity and prediction accuracy must be considered when building a decision tree (DT) from this kind of data. This paper proposes a novel classification algorithm for learning DT classifiers from data with structured continuous labels. This approach considers the distribution of labels throughout the hierarchical structure during the construction of trees without requiring discretization in the preprocessing stage. We compared the results of the proposed method with those of the C4.5 algorithm using eight real data sets. The empirical results indicate that the proposed method outperforms the C4.5 algorithm with regard to prediction accuracy, prediction specificity, and computational complexity.
關聯 IEEE Transactions on Systems, Man, and Cybernetics, 43(6), 1734 - 1746
資料類型 article
DOI http://dx.doi.org/10.1109/TSMCB.2012.2229269
dc.contributor 企管系en_US
dc.creator (作者) Hu, Hsiao-Wei;Chen, Yen-Liang ;Tang, Kweien_US
dc.date (日期) 2013.01en_US
dc.date.accessioned 16-Oct-2014 17:52:42 (UTC+8)-
dc.date.available 16-Oct-2014 17:52:42 (UTC+8)-
dc.date.issued (上傳時間) 16-Oct-2014 17:52:42 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/70629-
dc.description.abstract (摘要) Structured continuous-label classification is a variety of classification in which the label is continuous in the data, but the goal is to classify data into classes that are a set of predefined ranges and can be organized in a hierarchy. In the hierarchy, the ranges at the lower levels are more specific and inherently more difficult to predict, whereas the ranges at the upper levels are less specific and inherently easier to predict. Therefore, both prediction specificity and prediction accuracy must be considered when building a decision tree (DT) from this kind of data. This paper proposes a novel classification algorithm for learning DT classifiers from data with structured continuous labels. This approach considers the distribution of labels throughout the hierarchical structure during the construction of trees without requiring discretization in the preprocessing stage. We compared the results of the proposed method with those of the C4.5 algorithm using eight real data sets. The empirical results indicate that the proposed method outperforms the C4.5 algorithm with regard to prediction accuracy, prediction specificity, and computational complexity.en_US
dc.format.extent 130 bytes-
dc.format.mimetype text/html-
dc.language.iso en_US-
dc.relation (關聯) IEEE Transactions on Systems, Man, and Cybernetics, 43(6), 1734 - 1746en_US
dc.subject (關鍵詞) Classification algorithms; data mining; decision trees (DTs)en_US
dc.title (題名) A novel decision tree method for structured continuous-label classificationen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1109/TSMCB.2012.2229269en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1109/TSMCB.2012.2229269en_US