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2023 | OriginalPaper | Chapter

A Novel Oversampling Technique for Imbalanced Credit Scoring Datasets

Authors : Sudhansu Ranjan Lenka, Sukant Kishoro Bisoy, Rojalina Priyadarshini, Jhalak Hota

Published in: Intelligent Systems and Machine Learning

Publisher: Springer Nature Switzerland

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Abstract

The imbalanced class distribution of credit-scoring datasets typically makes the learning algorithms ineffective. In this study, NOSTE is proposed, a novel oversampling technique. It first identifies the informative minority instances by eliminating the noisy samples from the minority subset. Then, weight is assigned to the informative minority instances by considering the density and distance factors. Finally, new minority instances are created by determining the average of two different minority instances to make the dataset balanced. In the experimental study, NOSTE performance is validated by conducting an extensive comparison with four popular oversampling methods using three credit-scoring datasets from the UCI repository. The results confirmed that the proposed method brings significant improvement in the classification in terms of F-measure and AUC (Area under the Curve).

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Title: A Novel Oversampling Technique for Imbalanced Credit Scoring Datasets
Authors: Sudhansu Ranjan Lenka
Sukant Kishoro Bisoy
Rojalina Priyadarshini
Jhalak Hota
Publisher: Springer Nature Switzerland
Book: Intelligent Systems and Machine Learning
Print ISBN: 978-3-031-35080-1

Electronic ISBN: 978-3-031-35081-8

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-3-031-35081-8_12

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