@article{IEEESMCC2013,
author = "Francisco Fernandez-Navarro and Pilar Campoy-Mu{\~n}oz and M{\'o}nica de la Paz Mar{\'i}n and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Xin Yao",
abstract = "The current European debt crisis has drawn considerable attention to credit rating agencies’ news about sovereign ratings. From a technical point of view, credit rating constitutes a
typical ordinal regression problem because credit rating agencies generally present a scale of risk composed several categories. This fact motivated the use of an ordinal regression approach for
addressing the problem of sovereign credit-rating in this paper. Therefore, the ranking of different classes will be taken into account for the design of the classifier. To do so, a novel model
is introduced in order to replicate sovereign rating, based on the Negative Correlation Learning framework. The methodology is fully described in the paper, and applied to the classification
of the 27 European countries’ sovereign rating during the 2007-2010 period based on Standard and Poor’s reports. The proposed technique seems to be competitive and robust enough to classify the sovereign ratings reported by this agency when compared to other existing well-known ordinal and nominal methods.",
doi = "10.1109/TSMCC.2013.2247595",
issn = "2168-2291",
journal = "IEEE Transactions on Cybernetics",
keywords = "country risk detection, negative correlation learning, neural networks, ordinal regression",
month = "December",
note = "JCR 2011",
number = "6",
pages = "2228-2240",
title = "{A}ddressing the {EU} sovereign ratings using an ordinal regression approach",
url = "http://dx.doi.org/10.1109/TSMCC.2013.2247595",
volume = "43",
year = "2013",
}