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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

Πλήρης Εγγραφή

URI: http://purl.tuc.gr/dl/dias/C879D2B4-301F-411D-A9F6-72EFA8BD6AE0
Έτος 2019
Τύπος Δημοσίευση σε Περιοδικό με Κριτές
Άδεια Χρήσης
Λεπτομέρειες
Βιβλιογραφική Αναφορά E. 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.2019.098967 https://doi.org/10.1504/IJDMMM.2019.098967
Εμφανίζεται στις Συλλογές
Δημοσιεύσεις σε Περιοδικά στην Κοινότητα Σχολή Μηχανικών Παραγωγής και Διοίκησης

Περίληψη

Credit 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.

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