TY  - JOUR
AU  - Sergio Escalera
AU  - Oriol Pujol
AU  - Petia Radeva
PY  - 2010//
TI  - Re-coding ECOCs without retraining
T2  - PRL
JO  - Pattern Recognition Letters
SP  - 555–562
VL  - 31
IS  - 7
PB  - Elsevier
N2  - A standard way to deal with multi-class categorization problems is by the combination of binary classifiers in a pairwise voting procedure. Recently, this classical approach has been formalized in the Error-Correcting Output Codes (ECOC) framework. In the ECOC framework, the one-versus-one coding demonstrates to achieve higher performance than the rest of coding designs. The binary problems that we train in the one-versus-one strategy are significantly smaller than in the rest of designs, and usually easier to be learnt, taking into account the smaller overlapping between classes. However, a high percentage of the positions coded by zero of the coding matrix, which implies a high sparseness degree, does not codify meta-class membership information. In this paper, we show that using the training data we can redefine without re-training, in a problem-dependent way, the one-versus-one coding matrix so that the new coded information helps the system to increase its generalization capability. Moreover, the new re-coding strategy is generalized to be applied over any binary code. The results over several UCI Machine Learning repository data sets and two real multi-class problems show that performance improvements can be obtained re-coding the classical one-versus-one and Sparse random designs compared to different state-of-the-art ECOC configurations.
UR  - http://dx.doi.org/10.1016/j.patrec.2009.12.002
N1  - MILAB;HUPBA
ID  - Sergio Escalera2010
ER  -