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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/74488

Title:	Hybrid ensembles of decision trees and artificial neural networks
Authors:	Hsu, Kuo-Wei 徐國偉
Contributors:	資科系
Keywords:	Classification algorithm;Classification performance;Ensemble learning;Group decision making process;Classification (of information);Cybernetics;Learning systems;Neural networks;Decision trees
Date:	2012
Issue Date:	2015-04-10 17:26:15 (UTC+8)
Abstract:	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.
Relation:	Proceeding - 2012 IEEE International Conference on Computational Intelligence and Cybernetics, CyberneticsCom 2012 10.1109/CyberneticsCom.2012.6381610
Data Type:	conference
DOI link:	http://dx.doi.org/10.1109/CyberneticsCom.2012.6381610
DOI:	10.1109/CyberneticsCom.2012.6381610
Appears in Collections:	[Department of Computer Science ] Proceedings

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