The aim of this paper is to evaluate the results in term of misclassification rate of two classification models, Logit and Classification Trees (Cart), in a credit scoring context. Due to the dependence of results on input variables we will take into account this aspect to evaluate the prediction performance. To improve the prediction capability of this two models, we have also applied two statistical techniques, bagging and boosting, to evaluate whether using these aggregated predictors can be reached a better performance in term of classification results. Our results indicate a better classification capability of Cart and the error rate of both models can be further reduced using aggregated predictors. Furthermore Cart avoids variables selection problem.

Caiazza, S., Borra, S. (2002). Comparative performance of credit scoring models using aggregated predictors. In Management Information Systems (pp.747-756). WITPress.

Comparative performance of credit scoring models using aggregated predictors

CAIAZZA, STEFANO;BORRA, SIMONE
2002-01-01

Abstract

The aim of this paper is to evaluate the results in term of misclassification rate of two classification models, Logit and Classification Trees (Cart), in a credit scoring context. Due to the dependence of results on input variables we will take into account this aspect to evaluate the prediction performance. To improve the prediction capability of this two models, we have also applied two statistical techniques, bagging and boosting, to evaluate whether using these aggregated predictors can be reached a better performance in term of classification results. Our results indicate a better classification capability of Cart and the error rate of both models can be further reduced using aggregated predictors. Furthermore Cart avoids variables selection problem.
Third International Conference on Data Mining, Data Mining III
Bologna
25 September 2002 through 27 September 2002
Wessex Institute of Technology
Rilevanza internazionale
Settore SECS-P/01 - Economia Politica
Settore SECS-S/02 - Statistica per La Ricerca Sperimentale e Tecnologica
English
Computational complexity; Costs; Economics; Error analysis; Fuzzy control; Knowledge engineering; Learning algorithms; Neural networks; Statistical methods; Basel committee; Linear discriminant analysis; Logit and Classification Trees; Rating system; Risk assessment
Intervento a convegno
Caiazza, S., Borra, S. (2002). Comparative performance of credit scoring models using aggregated predictors. In Management Information Systems (pp.747-756). WITPress.
Caiazza, S; Borra, S
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/53332
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