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Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble

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  • Hong Wang
  • Qingsong Xu
  • Lifeng Zhou

Abstract

Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. In this research, the data is first balanced and diversified by clustering and bagging algorithms. Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. We show that the proposed algorithm outperforms popular credit scoring models such as decision tree, Lasso-logistic regression and random forests in terms of AUC and F-measure. We also provide two importance measures for the proposed model to identify important variables in the data.

Suggested Citation

  • Hong Wang & Qingsong Xu & Lifeng Zhou, 2015. "Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-20, February.
    • Handle: RePEc:plo:pone00:0117844
    DOI: 10.1371/journal.pone.0117844
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    References listed on IDEAS

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    Cited by:

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    2. Caporin, Massimiliano & Poli, Francesco, 2022. "News and intraday jumps: Evidence from regularization and class imbalance," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    3. Martin Leo & Suneel Sharma & K. Maddulety, 2019. "Machine Learning in Banking Risk Management: A Literature Review," Risks, MDPI, vol. 7(1), pages 1-22, March.
    4. Bauer, Kevin & Pfeuffer, Nicolas & Abdel-Karim, Benjamin M. & Hinz, Oliver & Kosfeld, Michael, 2020. "The terminator of social welfare? The economic consequences of algorithmic discrimination," SAFE Working Paper Series 287, Leibniz Institute for Financial Research SAFE.
    5. Sun, Yue & Chai, Nana & Dong, Yizhe & Shi, Baofeng, 2022. "Assessing and predicting small industrial enterprises’ credit ratings: A fuzzy decision-making approach," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1158-1172.
    6. Yajiao Tang & Junkai Ji & Yulin Zhu & Shangce Gao & Zheng Tang & Yuki Todo, 2019. "A Differential Evolution-Oriented Pruning Neural Network Model for Bankruptcy Prediction," Complexity, Hindawi, vol. 2019, pages 1-21, August.
    7. Dimitrios Nikolaidis & Michalis Doumpos, 2022. "Credit Scoring with Drift Adaptation Using Local Regions of Competence," SN Operations Research Forum, Springer, vol. 3(4), pages 1-28, December.
    8. Yasmin Agueda Rios-Solis & Mario Alberto Saucedo-Espinosa & Gabriel Arturo Caballero-Robledo, 2017. "Repayment policy for multiple loans," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-12, April.

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