Sampling Based Average Classifier Fusion
Joint Authors
Hou, Jian
Karimi, Hamid Reza
Liu, Wei-Xue
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-24
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Hou, Jian& Liu, Wei-Xue& Karimi, Hamid Reza. 2014. Sampling Based Average Classifier Fusion. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-466586
Modern Language Association (MLA)
Hou, Jian…[et al.]. Sampling Based Average Classifier Fusion. Mathematical Problems in Engineering No. 2014 (2014), pp.1-6.
https://search.emarefa.net/detail/BIM-466586
American Medical Association (AMA)
Hou, Jian& Liu, Wei-Xue& Karimi, Hamid Reza. Sampling Based Average Classifier Fusion. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-466586
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-466586