@Article{SergioEscalera2010,
author="Sergio Escalera
and Oriol Pujol
and Petia Radeva",
title="Re-coding ECOCs without retraining",
journal="Pattern Recognition Letters",
year="2010",
publisher="Elsevier",
volume="31",
number="7",
pages="555--562",
abstract="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.",
optnote="MILAB;HUPBA",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1338), last updated on Thu, 18 Jan 2018 12:02:15 +0100",
doi="10.1016/j.patrec.2009.12.002",
opturl="http://dx.doi.org/10.1016/j.patrec.2009.12.002"
}