%0 Conference Proceedings
%T Error Correcting Output Codes for multiclass classification: Application to two image vision problems
%A Mohammad Ali Bagheri
%A Qigang Gao
%A Sergio Escalera
%B 16th symposium on Artificial Intelligence & Signal Processing
%D 2012
%I IEEE Xplore
%@ 978-1-4673-1478-7
%F Mohammad Ali Bagheri2012
%O HuPBA;MILAB
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2042), last updated on Mon, 03 Jun 2024 08:40:25 +0200
%X Error-correcting output codes (ECOC) represents a powerful framework to deal with multiclass classification problems based on combining binary classifiers. The key factor affecting the performance of ECOC methods is the independence of binary classifiers, without which the ECOC method would be ineffective. In spite of its ability on classification of problems with relatively large number of classes, it has been applied in few real world problems. In this paper, we investigate the behavior of the ECOC approach on two image vision problems: logo recognition and shape classification using Decision Tree and AdaBoost as the base learners. The results show that the ECOC method can be used to improve the classification performance in comparison with the classical multiclass approaches.
%U http://refbase.cvc.uab.es/files/BGE2012b.pdf
%U http://dx.doi.org/10.1109/AISP.2012.6313800
%P 508-513