Decision tree induction with a constrained number of leaf nodes

NCUIR > School of Management at National Central University > Department of Information Management > journal & Dissertation > Item 987654321/106558

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/106558

Title:	Decision tree induction with a constrained number of leaf nodes
Authors:	陳彥良;Wu, Chia-Chi;Chen, Yen-Liang;Liu, Yi-Hung;Yang, Xiang-Yu
Contributors:	管理學院資訊管理學系
Keywords:	Accuracy;Algorithms;Artificial Intelligence;Classification;Classifiers;Computer Science;Constraints;Construction specifications;Data mining;Decision trees;Intelligence;Machines;Manufacturing;Mechanical Engineering;Processes;Usability
Date:	2016-10-01
Issue Date:	2026-04-23 13:28:46 (UTC+8)
Publisher:	Springer Netherlands;New York: Springer US
Abstract:	摘要： With the advantages of being easy to understand and efficient to compute, the decision tree method has long been one of the most popular classifiers. Decision trees constructed with existing approaches, however, tend to be huge and complex, and consequently are difficult to use in practical applications. In this study, we deal with the problem of tree complexity by allowing users to specify the number of leaf nodes, and then construct a decision tree that allows maximum classification accuracy with the given number of leaf nodes. A new algorithm, the Size Constrained Decision Tree (SCDT), is proposed with which to construct a decision tree, paying close attention on how to efficiently use the limited number of leaf nodes. Experimental results show that the SCDT method can successfully generate a simpler decision tree and offers better accuracy. 其他題名： Appl Intell 出版者： New York: Springer US 出版日期： 2016-10 出處： Applied intelligence (Dordrecht, Netherlands), 2016-10, Vol.45 (3), p.673-685 資源來源： ABI/INFORM Collection 版權： Springer Science+Business Media New York 2016 識別號： ISSN: 0924-669X 識別號： EISSN: 1573-7497 識別號： DOI: 10.1007/s10489-016-0785-z
Appears in Collections:	[Department of Information Management] journal & Dissertation

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