Low Default Credit Scoring using Two-class Non-parametric Kernel Density Estimation

被引：0

作者：

Rademeyer, Estian ^{[1
]}

van der Walt, Christiaan M. ^{[2
]}

de Waal, Alta ^{[1
]}

机构：

[1] Univ Pretoria, Dept Stat, Pretoria, South Africa

[2] CSIR, Modelling & Digital Sci, Pretoria, South Africa

来源：

2016 PATTERN RECOGNITION ASSOCIATION OF SOUTH AFRICA AND ROBOTICS AND MECHATRONICS INTERNATIONAL CONFERENCE (PRASA-ROBMECH) | 2016年

基金：

新加坡国家研究基金会;

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper investigates the performance of two-class classification credit scoring data sets with low default ratios. The standard two-class parametric Gaussian and non-parametric Parzen classifiers are extended, using Bayes' rule, to include either a class imbalance or a Bernoulli prior. This is done with the aim of addressing the low default probability problem. Furthermore, the performance of Parzen classification with Silverman and Minimum Leave-one-out Entropy (MLE) Gaussian kernel bandwidth estimation is also investigated.

引用

页数：6

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