Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Evolutionary design of multiclass support vector machines
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - VIII Brazilian Symposium on Neural Networks
On the Decoding Process in Ternary Error-Correcting Output Codes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimal design of error-correcting output codes
Pattern Recognition Letters
Evolving Output Codes for Multiclass Problems
IEEE Transactions on Evolutionary Computation
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In this work, we propose a novel Genetic Inspired Error Correcting Output Codes (ECOC) Optimization, which looks for an efficient problem-dependent encoding of the multi-class task with high generalization performance. This optimization procedure is based on novel ECOC-Compliant crossover, mutation, and extension operators, which guide the optimization process to promising regions of the search space. The results on several public datasets show significant performance improvements as compared to state-of-the-art ECOC strategies.