文档介绍：Improving Personal Credit Scoring with HLVQ-C
A. Vieira1, João Duarte1, B. Ribeiro2, and . Neves3
1 ISEP, Rua de S. Tomé, 4200 Porto, Portugal
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2 Department of Informatics Engineering, University of Coimbra, P-3030-290
Coimbra, Portugal
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3 ISEG - School of Economics, Rua Miguel Lupi 20, 1249-078 Lisboa, Portugal
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Abstract. In this paper we study personal credit scoring using several machine
learning algorithms: Multilayer Perceptron, Logistic Regression, Support Vec-
tor Machines, AddaboostM1 and Hidden Layer Learning Vector Quantization.
The scoring models were tested on a large dataset from a Portuguese bank. Re-
sults are benchmarked against traditional methods under consideration for
commercial applications. A measure of the usefulness of a scoring model is pre-
sented and we show that HLVQ-C is the most accurate model.
1 Introduction
Quantitative credit scoring models have been developed for the credit granting deci-
sion in order to classify applications as ‘good’ or `bad’, the latest being loosely de-
fined as a group with a high likelihood of defaulting on the financial obligation.
It is very important to have accurate models to identify bad performers. Even a
small fraction increase in credit scoring accuracy is important. Linear discriminant
analysis st