Combining classifiers for credit risk prediction |
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Authors: | Bhekisipho Twala |
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Institution: | CSIR, Modelling and Digital Sciences Unit, P.O. Box 395, Pretoria 0001, South Africa |
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Abstract: | Credit risk prediction models seek to predict quality factors such as whether an individual will default (bad applicant) on
a loan or not (good applicant). This can be treated as a kind of machine learning (ML) problem. Recently, the use of ML algorithms
has proven to be of great practical value in solving a variety of risk problems including credit risk prediction. One of the
most active areas of recent research in ML has been the use of ensemble (combining) classifiers. Research indicates that ensemble
individual classifiers lead to a significant improvement in classification performance by having them vote for the most popular
class. This paper explores the predicted behaviour of five classifiers for different types of noise in terms of credit risk
prediction accuracy, and how could such accuracy be improved by using pairs of classifier ensembles. Benchmarking results
on five credit datasets and comparison with the performance of each individual classifier on predictive accuracy at various
attribute noise levels are presented. The experimental evaluation shows that the ensemble of classifiers technique has the
potential to improve prediction accuracy. |
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Keywords: | Supervised learning statistical pattern recognition ensemble credit risk prediction |
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