Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Stochastic Organization of Output Codes in Multiclass Learning Problems
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECOC-ONE: A Novel Coding and Decoding Strategy
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Data-driven decomposition for multi-class classification
Pattern Recognition
An incremental node embedding technique for error correcting output codes
Pattern Recognition
Evolutionary design of multiclass support vector machines
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - VIII Brazilian Symposium on Neural Networks
Subclass Problem-Dependent Design for Error-Correcting Output Codes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blurred Shape Model for binary and grey-level symbol recognition
Pattern Recognition Letters
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
On the Decoding Process in Ternary Error-Correcting Output Codes
IEEE Transactions on Pattern Analysis and Machine Intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Primitive segmentation in old handwritten music scores
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
Minimal design of error-correcting output codes
Pattern Recognition Letters
Evolving Output Codes for Multiclass Problems
IEEE Transactions on Evolutionary Computation
Design of reject rules for ECOC classification systems
Pattern Recognition
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Genetic Algorithms (GA) have been previously applied to Error-Correcting Output Codes (ECOC) in state-of-the-art works in order to find a suitable coding matrix. Nevertheless, none of the presented techniques directly take into account the properties of the ECOC matrix. As a result the considered search space is unnecessarily large. In this paper, a novel Genetic strategy to optimize the ECOC coding step is presented. This novel strategy redefines the usual crossover and mutation operators in order to take into account the theoretical properties of the ECOC framework. Thus, it reduces the search space and lets the algorithm to converge faster. In addition, a novel operator that is able to enlarge the code in a smart way is introduced. The novel methodology is tested on several UCI datasets and four challenging computer vision problems. Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers. Finally, classification performance per dichotomizer results shows that the novel proposal is able to obtain similar or even better results while defining a more compact number of dichotomies and SVs compared to state-of-the-art approaches.