Journal ArticleDOI
Multiple classifier application to credit risk assessment
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TLDR
The experimental evaluation shows that the ensemble of classifiers technique has the potential to improve prediction accuracy, and how such accuracy could be improved by using classifier ensembles.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 such accuracy could be improved by using classifier ensembles. Benchmarking results on four 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.read more
Citations
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Journal ArticleDOI
A survey of multiple classifier systems as hybrid systems
TL;DR: An up-to-date survey on multiple classifier system (MCS) from the point of view of Hybrid Intelligent Systems is presented, providing a vision of the spectrum of applications that are currently being developed.
Journal ArticleDOI
Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research
TL;DR: The study of Baesens et al. (2003) is updated and several novel classification algorithms to the state-of-the-art in credit scoring are compared, providing an independent assessment of recent scoring methods and offering a new baseline to which future approaches can be compared.
Journal ArticleDOI
A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring
TL;DR: A sequential ensemble credit scoring model based on a variant of gradient boosting machine (i.e., extreme gradient boosting (XGBoost) is proposed, which demonstrates that Bayesian hyper-parameter optimization performs better than random search, grid search, and manual search.
Journal ArticleDOI
Machine Learning in Financial Crisis Prediction: A Survey
TL;DR: This paper presents the current achievements and limitations associated with the development of bankruptcy-prediction and credit-scoring models employing machine learning, and provides suggestions for future research.
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