Abstract
A common way to model multi-class classification problems is by means of Error-Correcting Output Codes (ECOC). Given a multi-class problem, the ECOC technique designs a codeword for each class, where each position of the code identifies the membership of the class for a given binary problem.A classification decision is obtained by assigning the label of the class with the closest code. In this paper, we overview the state-of-the-art on ECOC designs and test them in real applications. Results on different multi-class data sets show the benefits of using the ensemble of classifiers when categorizing objects in images.
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Escalera, S., Tax, D.M.J., Pujol, O., Radeva, P., Duin, R.P.W. (2011). Multi-class Classification in Image Analysis via Error-Correcting Output Codes. In: Kwaśnicka, H., Jain, L.C. (eds) Innovations in Intelligent Image Analysis. Studies in Computational Intelligence, vol 339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17934-1_2
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