Boosting in classifier fusion vs. fusing boosted classifiers

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.

[1]  Adam Krzyżak,et al.  Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..

[2]  Nageswara S. V. Rao,et al.  On Fusers that Perform Better than Best Sensor , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[4]  Edward Y. Chang,et al.  Distance-function design and fusion for sequence data , 2004, CIKM '04.

[5]  Naonori Ueda,et al.  Optimal Linear Combination of Neural Networks for Improving Classification Performance , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Josef Kittler,et al.  Combining classifiers: A theoretical framework , 1998, Pattern Analysis and Applications.

[7]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[8]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[9]  L. Breiman.  Arcing Classifiers , 1998 .

[10]  Josef Kittler,et al.  A Framework for Classifier Fusion: Is It Still Needed? , 2000, SSPR/SPR.

[11]  Sherif Hashem,et al.  Optimal Linear Combinations of Neural Networks , 1997, Neural Networks.

[12]  L. Breiman.  Arcing classifier (with discussion and a rejoinder by the author) , 1998 .

[13]  Philip S. Yu,et al.  Is random model better? On its accuracy and efficiency , 2003, Third IEEE International Conference on Data Mining.

[14]  Ching Y. Suen,et al.  A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  James C. Bezdek,et al.  Decision templates for multiple classifier fusion: an experimental comparison , 2001, Pattern Recognit..

[16]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..