Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Subclass Problem-Dependent Design for Error-Correcting Output Codes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Evolving Output Codes for Multiclass Problems
IEEE Transactions on Evolutionary Computation
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Error-correcting output codes (ECOC) is an effective method to perform multi-classification via decomposing a multi-classification problem into many binary classification tasks, and then integrating the outputs of the subtasks into a whole decision. The researches on applying ECOC to multi-classification mainly focus on how to improve the correcting ability of output codes and how to enhance the classification effectiveness of ECOC. This paper addresses a simple but interesting and significant case of ECOC, the shortest ECOC, to perform fast multi-classification at the cost of sacrificing a very small classification precision. The strategy of balancing the positive and negative examples for each binary classifier of ECOC and the method of finding the optimal permutation of all original classes are further given. Preliminary experimental results show, the shortest ECOC uses fewest binary classifiers but can still obtain comparable or close classification precisions with several traditional encoding methods of ECOC.