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Model combination for bankruptcy prediction and credit risk assessment: A stacked generalization approach

Zopounidis Konstantinos, Doumpos, Michael

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/97DB93E9-C274-41E5-B80F-1FAD79A9FC42-
Αναγνωριστικόhttps://doi.org/10.1007/s10479-006-0120-x-
Γλώσσαen-
Μέγεθος18 pagesen
ΤίτλοςModel combination for bankruptcy prediction and credit risk assessment: A stacked generalization approachen
ΔημιουργόςZopounidis Konstantinosen
ΔημιουργόςΖοπουνιδης Κωνσταντινοςel
ΔημιουργόςDoumpos, Michaelen
ΕκδότηςKluweren
ΠερίληψηThe development of credit risk assessment models is often considered within a classification context. Recent studies on the development of classification models have shown that a combination of methods often provides improved classification results compared to a single-method approach. Within this context, this study explores the combination of different classification methods in developing efficient models for credit risk assessment. A variety of methods are considered in the combination, including machine learning approaches and statistical techniques. The results illustrate that combined models can outperform individual models for credit risk analysis. The analysis also covers important issues such as the impact of using different parameters for the combined models, the effect of attribute selection, as well as the effects of combining strong or weak models.en
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2015-10-28-
Ημερομηνία Δημοσίευσης2007-
Θεματική ΚατηγορίαCapability, Financial (Financial literacy)en
Θεματική ΚατηγορίαFinancial capability (Financial literacy)en
Θεματική ΚατηγορίαLiteracy, Financialen
Θεματική Κατηγορίαfinancial literacyen
Θεματική Κατηγορίαcapability financial financial literacyen
Θεματική Κατηγορίαfinancial capability financial literacyen
Θεματική Κατηγορίαliteracy financialen
Βιβλιογραφική ΑναφοράM. Doumpos, C. Zopounidis," Model combination for bankruptcy prediction and credit risk assessment: A stacked generalization approach,"Annals of Oper.Res., vol 151, no. 1,pp. 289- 306,April 2007.doi:10.1007/s10479-006-0120-xen

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