Boosting in Classifier Fusion vs. Fusing Boosted Classifiers

Authors

Costin Barbu, Tulane UniversityFollow
Kun Zhang, Xavier University of LouisianaFollow
Jing Peng, Montclair State UniversityFollow
Bill Buckles, University of North Texas

Document Type

Conference Proceeding

Publication Date

12-1-2005

Abstract

In this paper we investigate the performance of boosting used for fusing various classifiers. We propose a new boosting - based algorithm for fusion and we show through empirical studies on texture image data sets that it outperforms existing SVM-based classifier fusion technique in terms of accuracy, computational efficiency and robustness.

DOI

10.1109/IRI-05.2005.1506495

Montclair State University Digital Commons Citation

Barbu, Costin; Zhang, Kun; Peng, Jing; and Buckles, Bill, "Boosting in Classifier Fusion vs. Fusing Boosted Classifiers" (2005). Department of Computer Science Faculty Scholarship and Creative Works. 150.
https://digitalcommons.montclair.edu/compusci-facpubs/150