JCP 2014 Vol.9(7): 1547-1552 ISSN: 1796-203X
doi: 10.4304/jcp.9.7.1547-1552
doi: 10.4304/jcp.9.7.1547-1552
On Adjustment Functions for Weight-Adjusted Voting-Based Ensembles of Classifiers
Kuo-Wei Hsu
Department of Computer Science, National Chengchi University, Taipei, Taiwan
Center for Computational Research and Applications, National Chengchi University, Taipei, Taiwan
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.
Index Terms—Classification, ensemble, voting
Center for Computational Research and Applications, National Chengchi University, Taipei, Taiwan
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.
Index Terms—Classification, ensemble, voting
Cite: Kuo-Wei Hsu, "On Adjustment Functions for Weight-Adjusted Voting-Based Ensembles of Classifiers," Journal of Computers vol. 9, no. 7, pp. 1547-1552, 2014.
General Information
ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
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