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Sample selection algorithms for credit risk modelling through data mining techniques

Protopapadakis Eftychios, Doumpos Michail, Doulamis Anastasios, Zopounidis Konstantinos, Niklis Dimitrios

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URIhttp://purl.tuc.gr/dl/dias/C879D2B4-301F-411D-A9F6-72EFA8BD6AE0-
Identifierhttps://doi.org/10.1504/IJDMMM.2019.098967-
Identifierhttp://www.inderscience.com/offer.php?id=98969-
Languageen-
Extent26 pagesen
TitleSample selection algorithms for credit risk modelling through data mining techniquesen
CreatorProtopapadakis Eftychiosen
CreatorΠρωτοπαπαδακης Ευτυχιοςel
CreatorDoumpos Michailen
CreatorΔουμπος Μιχαηλel
CreatorDoulamis Anastasiosen
CreatorΔουλαμης Αναστασιοςel
CreatorZopounidis Konstantinosen
CreatorΖοπουνιδης Κωνσταντινοςel
CreatorNiklis Dimitriosen
CreatorΝικλης Δημητριοςel
PublisherInderscienceen
Content SummaryCredit risk assessment is a very challenging and important problem in the domain of financial risk management. The development of reliable credit rating/scoring models is of paramount importance in this area. There are different algorithms and approaches for constructing such models to classify credit applicants (firms or individuals) into risk classes. Reliable sample selection is crucial for this task. The aim of this paper is to examine the effectiveness of sample selection schemes in combination with different classifiers for constructing reliable default prediction models. We consider different algorithms to select representative cases and handle class imbalances. Empirical results are reported for a dataset of Greek companies from the commercial sector.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2020-11-03-
Date of Publication2019-
SubjectClassificationen
SubjectCredit risk modellingen
SubjectData miningen
SubjectSamplingen
Bibliographic CitationE. Protopapadakis, D. Niklis, M. Doumpos, A. Doulamis and C. Zopounidis, "Sample selection algorithms for credit risk modelling through data mining techniques," Int. J. Data Min. Model. Manag., vol. 11, no. 2, pp. 103-128, Feb. 2019. doi: 10.1504/IJDMMM.2019.098967en

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