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政大機構典藏 > 資訊學院 > 資訊科學系 > 期刊論文 > Item 140.119/66287

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/66287

Title:	Improving Bagging Performance through Multi-Algorithm Ensembles
Authors:	徐國偉 Hsu, Kuo-Wei
Contributors:	資科系
Keywords:	Sensory evoked potential;SEP;Auditory evoked potential;AEP;ERP;MeCP2;Preclinical model;Mouse;Gamma oscillation
Date:	2012.07
Issue Date:	2014-05-27 11:25:10 (UTC+8)
Abstract:	Bagging establishes a committee of classifiers first and then aggregates their outcomes through majority voting. Bagging has attracted considerable research interest and been applied in various application domains. Its advantages include an increased capability of handling small data sets, less sensitivity to noise or outliers, and a parallel structure for efficient implementations. However, it has been found to be less accurate than some other ensemble methods. In this paper, we propose an approach that improves bagging through the employment of multiple classification algorithms in ensembles. Our approach preserves the parallel structure of bagging and improves the accuracy of bagging. As a result, it unlocks the power and expands the user base of bagging
Relation:	Frontiers of Computer Science, 6(5), 498-512
Data Type:	article
DOI 連結:	http://dx.doi.org/10.1007/978-3-642-28320-8_40
DOI:	10.1007/978-3-642-28320-8_40
Appears in Collections:	[資訊科學系] 期刊論文

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