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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 369613, 6 pages
http://dx.doi.org/10.1155/2014/369613
Research Article

Sampling Based Average Classifier Fusion

1School of Information Science and Technology, Bohai University, Jinzhou 121013, China
2Department of Engineering, Faculty of Engineering and Science, University of Agder, 4898 Grimstad, Norway

Received 22 December 2013; Accepted 22 January 2014; Published 24 February 2014

Academic Editor: Xudong Zhao

Copyright © 2014 Jian Hou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Classifier fusion is used to combine multiple classification decisions and improve classification performance. While various classifier fusion algorithms have been proposed in literature, average fusion is almost always selected as the baseline for comparison. Little is done on exploring the potential of average fusion and proposing a better baseline. In this paper we empirically investigate the behavior of soft labels and classifiers in average fusion. As a result, we find that; by proper sampling of soft labels and classifiers, the average fusion performance can be evidently improved. This result presents sampling based average fusion as a better baseline; that is, a newly proposed classifier fusion algorithm should at least perform better than this baseline in order to demonstrate its effectiveness.