Abstract
Credit scoring is an imbalanced binary classification problem, where the number of instances of bad customers is much less than that of good customers. Traditional classification algorithms may not give an effective performance while dealing with these imbalanced datasets, especially when classifying the minority class instances. To overcome the imbalanced problems, different undersampling and oversampling techniques have been proposed to reduce the majority class instances and oversample the minority class instances, respectively. In this paper, the clustering-based undersampling technique (CUTE) is proposed to tackle the imbalanced credit scoring problems. CUTE implements a new strategy to compute the representativeness of each member of the majority class subset. Here, the proposed model is compared with four traditional resampling techniques using two credit scoring datasets. Additionally, the performance of the proposed model consistently improves in different measures, such as accuracy, precision, recall, Fscore, and AUC.
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Lenka, S.R., Bisoy, S.K., Priyadarshini, R., Nayak, B. (2022). Representative-Based Cluster Undersampling Technique for Imbalanced Credit Scoring Datasets. In: Roy, S., Sinwar, D., Perumal, T., Slowik, A., Tavares, J.M.R.S. (eds) Innovations in Computational Intelligence and Computer Vision . Advances in Intelligent Systems and Computing, vol 1424. Springer, Singapore. https://doi.org/10.1007/978-981-19-0475-2_11
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DOI: https://doi.org/10.1007/978-981-19-0475-2_11
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