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Credit rating modelling by kernel-based approaches with supervised and semi-supervised learning

  • EANN 2009
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Abstract

This paper presents the modelling possibilities of kernel-based approaches to a complex real-world problem, i.e. corporate and municipal credit rating classification. Based on a model design that includes data pre-processing, the labelling of individual parameter vectors using expert knowledge, the design of various support vector machines with supervised learning as well as kernel-based approaches with semi-supervised learning, this modelling is undertaken in order to classify objects into rating classes. The results show that the rating classes assigned to bond issuers can be classified with high classification accuracy using a limited subset of input variables. This holds true for kernel-based approaches with both supervised and semi-supervised learning.

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Acknowledgments

This work was supported by a scientific research grant by the Czech Science Foundation, under Grant No: 402/09/P090 with the title Modelling of Municipal Finance by Computational Intelligence Methods and Grant No: 402/08/0849 with the title Model of Sustainable Regional Development Management.

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Authors and Affiliations

  1. Institute of System Engineering and Informatics, Faculty of Economics and Administration, University of Pardubice, Studentská 84, 532 10, Pardubice, Czech Republic

    Petr Hájek & Vladimír Olej

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  1. Petr Hájek
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  2. Vladimír Olej
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Correspondence to Petr Hájek.

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Hájek, P., Olej, V. Credit rating modelling by kernel-based approaches with supervised and semi-supervised learning. Neural Comput & Applic 20, 761–773 (2011). https://doi.org/10.1007/s00521-010-0495-0

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