An Empirical Comparison of Classification Algorithms for Imbalanced Credit Scoring Datasets

The profitability of banks is highly dependent on credit scoring models, which support decision making to approve a loan to a customer. State-of-the-art credit scoring models are based on learning methods. These methods need to cope with the problem of imbalanced classes since credit scoring dataset...

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Bibliographic Details

Published in:	2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) pp. 747 - 754
Main Authors:	Soares de Melo Junior, Leopoldo, Nardini, Franco Maria, Renso, Chiara, Fernandes de Macedo, Jose Antonio
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-12-2019
Subjects:	Area measurement Benchmarking Biological system modeling Classification Computational modeling Credit scoring Decision trees Imbalanced datasets Machine learning Support vector machines Training
Online Access:	Get full text
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