Improving Personal Credit Scoring with HLVQ-C

被引：0

作者：

Vieira, A. ^{[1
]}

Duarte, Joao ^{[1
]}

Ribeiro, B. ^{[2
]}

Neves, J. C. ^{[3
]}

机构：

[1] ISEP, Rua S Tome, P-4200 Oporto, Portugal

[2] Univ Coimbra, Dept Informat Engn, P-3030290 Coimbra, Portugal

[3] ISEG, Sch Econ, P-1249078 Lisbon, Portugal

来源：

ADVANCES IN NEURO-INFORMATION PROCESSING, PT II | 2009年 / 5507卷

关键词：

NEURAL NETWORKS; MODELS;

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：

In this paper we Study personal credit scoring using several machine learning algorithms: Multilayer Perceptron, Logistic Regression, Support Vector Machines, AddaboostM1 and Hidden Layer Learning Vector Quantization. The scoring models were tested on a large dataset from a Portuguese bank. Results are benchmarked against traditional methods under consideration for commercial applications. A measure of the usefulness of a scoring model is presented and we show that HLVQ-C is the most accurate model.

引用

页码：97 / +

页数：2

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