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政大機構典藏 > 資訊學院 > 資訊科學系 > 期刊論文 > Item 140.119/70680

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/70680

Title:	On Adjustment Functions for Weight-Adjusted Voting-Based Ensembles of Classifiers
Authors:	徐國偉 Hsu, Kuo-Wei
Contributors:	資科系
Keywords:	Classificatio;ensemble;voting
Date:	2014.07
Issue Date:	2014-10-20 18:21:29 (UTC+8)
Abstract:	An ensemble of classifiers is a system consisting of multiple member classifiers which are trained individually and whose outcomes are aggregated into an overall outcome for a testing data instance. Voting is a common approach used to aggregate outcomes generated by member classifiers. Ensembles based on weighted voting have been studied for some time. However, the focus of most studies is more on weight assignment rather than on weight adjustment, whose basic idea is to increase the weights of votes from member classifiers performing better on data instances of higher difficulty. In this paper, we present our study on adjustment functions in each of which both the performance of a member classifier and the difficulty of a data set are determined nonlinearly. We report results from experiments conducted on several data sets, demonstrating the potential of the studied functions.
Relation:	Journal of Computers (JCP), 9(7), 1547-1552
Data Type:	article
DOI 連結:	http://dx.doi.org/10.4304/jcp.9.7.1547-1552
DOI:	10.4304/jcp.9.7.1547-1552
Appears in Collections:	[資訊科學系] 期刊論文

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