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題名 Hybrid ensembles of decision trees and artificial neural networks
作者 Hsu, Kuo-Wei
徐國偉
貢獻者 資科系
關鍵詞 Classification algorithm; Classification performance; Ensemble learning; Group decision making process; Classification (of information); Cybernetics; Learning systems; Neural networks; Decision trees
日期 2012
上傳時間 10-Apr-2015 17:26:15 (UTC+8)
摘要 Ensemble learning is inspired by the human group decision making process, and it has been found beneficial in various application domains. Decision tree and artificial neural network are two popular types of classification algorithms often used to construct classic ensembles. Recently, researchers proposed to use the mixture of both types to construct hybrid ensembles. However, researchers use decision trees and artificial neural networks together in an ensemble without further discussion. The focus of this paper is on the hybrid ensemble constructed by using decision trees and artificial neural networks simultaneously. The goal of this paper is not only to show that the hybrid ensemble can achieve comparable or even better classification performance, but also to provide an explanation of why it works. © 2012 IEEE.
關聯 Proceeding - 2012 IEEE International Conference on Computational Intelligence and Cybernetics, CyberneticsCom 2012
10.1109/CyberneticsCom.2012.6381610
資料類型 conference
DOI http://dx.doi.org/10.1109/CyberneticsCom.2012.6381610
dc.contributor 資科系
dc.creator (作者) Hsu, Kuo-Wei
dc.creator (作者) 徐國偉zh_TW
dc.date (日期) 2012
dc.date.accessioned 10-Apr-2015 17:26:15 (UTC+8)-
dc.date.available 10-Apr-2015 17:26:15 (UTC+8)-
dc.date.issued (上傳時間) 10-Apr-2015 17:26:15 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/74488-
dc.description.abstract (摘要) Ensemble learning is inspired by the human group decision making process, and it has been found beneficial in various application domains. Decision tree and artificial neural network are two popular types of classification algorithms often used to construct classic ensembles. Recently, researchers proposed to use the mixture of both types to construct hybrid ensembles. However, researchers use decision trees and artificial neural networks together in an ensemble without further discussion. The focus of this paper is on the hybrid ensemble constructed by using decision trees and artificial neural networks simultaneously. The goal of this paper is not only to show that the hybrid ensemble can achieve comparable or even better classification performance, but also to provide an explanation of why it works. © 2012 IEEE.
dc.format.extent 176 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Proceeding - 2012 IEEE International Conference on Computational Intelligence and Cybernetics, CyberneticsCom 2012
dc.relation (關聯) 10.1109/CyberneticsCom.2012.6381610
dc.subject (關鍵詞) Classification algorithm; Classification performance; Ensemble learning; Group decision making process; Classification (of information); Cybernetics; Learning systems; Neural networks; Decision trees
dc.title (題名) Hybrid ensembles of decision trees and artificial neural networks
dc.type (資料類型) conferenceen
dc.identifier.doi (DOI) 10.1109/CyberneticsCom.2012.6381610en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1109/CyberneticsCom.2012.6381610en_US