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政大機構典藏 > 商學院 > 企業管理學系 > 期刊論文 > Item 140.119/70629

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/70629

Title:	A novel decision tree method for structured continuous-label classification
Authors:	Hu, Hsiao-Wei;Chen, Yen-Liang;Tang, Kwei
Contributors:	企管系
Keywords:	Classification algorithms;data mining;decision trees (DTs)
Date:	2013.01
Issue Date:	2014-10-16 17:52:42 (UTC+8)
Abstract:	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.
Relation:	IEEE Transactions on Systems, Man, and Cybernetics, 43(6), 1734 - 1746
Data Type:	article
DOI 連結:	http://dx.doi.org/10.1109/TSMCB.2012.2229269
DOI:	10.1109/TSMCB.2012.2229269
Appears in Collections:	[企業管理學系] 期刊論文

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