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. © 2014 Jian Hou et al.

Sampling based average classifier fusion

KARIMI, HAMID REZA
2014-01-01

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. © 2014 Jian Hou et al.
2014
Mathematics (all); Engineering (all)
File in questo prodotto:
File Dimensione Formato  
11311-1028725_Karimi.pdf

accesso aperto

: Publisher’s version
Dimensione 2.11 MB
Formato Adobe PDF
2.11 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1028725
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 2
social impact