Parameter estimation for the imbalanced credit scoring data using AUC maximization

- Journal title : Korean Journal of Applied Statistics
- Volume 29, Issue 2, 2016, pp.309-319
- Publisher : The Korean Statistical Society
- DOI : 10.5351/KJAS.2016.29.2.309

Title & Authors

Parameter estimation for the imbalanced credit scoring data using AUC maximization

Hong, C.S.; Won, C.H.;

Hong, C.S.; Won, C.H.;

Abstract

For binary classification models, we consider a risk score that is a function of linear scores and estimate the coefficients of the linear scores. There are two estimation methods: one is to obtain MLEs using logistic models and the other is to estimate by maximizing AUC. AUC approach estimates are better than MLEs when using logistic models under a general situation which does not support logistic assumptions. This paper considers imbalanced data that contains a smaller number of observations in the default class than those in the non-default for credit assessment models; consequently, the AUC approach is applied to imbalanced data. Various logit link functions are used as a link function to generate imbalanced data. It is found that predicted coefficients obtained by the AUC approach are equivalent to (or better) than those from logistic models for low default probability - imbalanced data.

Keywords

discrimination;link;risk;ROC;threshold;

Language

Korean

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