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Vol.7, No.1, February 2018. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Cost-Sensitive Learning from Imbalanced Datasets for Retail Credit Risk Assessment
Stjepan Oreški, Goran Oreški
© 2018 Stjepan Oreški, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 7, Issue 1, Pages 59-73, ISSN 2217-8309, DOI: 10.18421/TEM71-08, February 2018.
Received: 14 November 2017
Abstract:
In the present study we propose a new classification technique based on genetic algorithm and neural network, optimized for the cost-sensitive measure and applied to retail credit risk assessment. The relative cost of misclassification, which properly accounts for different misclassification costs of minority and majority classes, is used as the primary evaluation measure. The test of the new algorithm is performed on Croatian and German retail credit datasets for seven different cost ratios. An empirical comparison with others in the literature presented models demonstrates the potential of the new technique in terms of misclassification costs.
Keywords – genetic algorithm; neural network; credit risk assessment; imbalanced datasets; misclassification cost. |
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