PT Unknown
AU Mohammad Ali Bagheri
   Qigang Gao
   Sergio Escalera
TI Error Correcting Output Codes for multiclass classification: Application to two image vision problems
BT 16th symposium on Artificial Intelligence & Signal Processing
PY 2012
BP 508
EP 513
DI 10.1109/AISP.2012.6313800
AB 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.
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