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A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring

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  • Koutanaei, Fatemeh Nemati
  • Sajedi, Hedieh
  • Khanbabaei, Mohammad

Abstract

Data mining techniques have numerous applications in credit scoring of customers in the banking field. One of the most popular data mining techniques is the classification method. Previous researches have demonstrated that using the feature selection (FS) algorithms and ensemble classifiers can improve the banks' performance in credit scoring problems. In this domain, the main issue is the simultaneous and the hybrid utilization of several FS and ensemble learning classification algorithms with respect to their parameters setting, in order to achieve a higher performance in the proposed model. As a result, the present paper has developed a hybrid data mining model of feature selection and ensemble learning classification algorithms on the basis of three stages. The first stage, as expected, deals with the data gathering and pre-processing. In the second stage, four FS algorithms are employed, including principal component analysis (PCA), genetic algorithm (GA), information gain ratio, and relief attribute evaluation function. In here, parameters setting of FS methods is based on the classification accuracy resulted from the implementation of the support vector machine (SVM) classification algorithm. After choosing the appropriate model for each selected feature, they are applied to the base and ensemble classification algorithms. In this stage, the best FS algorithm with its parameters setting is indicated for the modeling stage of the proposed model. In the third stage, the classification algorithms are employed for the dataset prepared from each FS algorithm. The results exhibited that in the second stage, PCA algorithm is the best FS algorithm. In the third stage, the classification results showed that the artificial neural network (ANN) adaptive boosting (AdaBoost) method has higher classification accuracy. Ultimately, the paper verified and proposed the hybrid model as an operative and strong model for performing credit scoring.

Suggested Citation

  • Koutanaei, Fatemeh Nemati & Sajedi, Hedieh & Khanbabaei, Mohammad, 2015. "A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring," Journal of Retailing and Consumer Services, Elsevier, vol. 27(C), pages 11-23.
    • Handle: RePEc:eee:joreco:v:27:y:2015:i:c:p:11-23
    DOI: 10.1016/j.jretconser.2015.07.003
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    References listed on IDEAS

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    2. Omar H. Fares & Irfan Butt & Seung Hwan Mark Lee, 2023. "Utilization of artificial intelligence in the banking sector: a systematic literature review," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 28(4), pages 835-852, December.
    3. Djonata Schiessl & Helison Bertoli Alves Dias & José Carlos Korelo, 2022. "Artificial intelligence in marketing: a network analysis and future agenda," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(3), pages 207-218, September.
    4. Ching-Chin Chern & Weng-U Lei & Kwei-Long Huang & Shu-Yi Chen, 2021. "A decision tree classifier for credit assessment problems in big data environments," Information Systems and e-Business Management, Springer, vol. 19(1), pages 363-386, March.
    5. Barboza, Flavio & Altman, Edward, 2024. "Predicting financial distress in Latin American companies: A comparative analysis of logistic regression and random forest models," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    6. Trivedi, Shrawan Kumar, 2020. "A study on credit scoring modeling with different feature selection and machine learning approaches," Technology in Society, Elsevier, vol. 63(C).
    7. Vahid Baradaran & Maryam Keshavarz, 2015. "An integrated approach of system dynamics simulation and fuzzy inference system for retailers’ credit scoring," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 28(1), pages 959-980, January.
    8. Zieba, Maciej & Härdle, Wolfgang Karl, 2016. "Beta-boosted ensemble for big credit scoring data," SFB 649 Discussion Papers 2016-052, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    9. Li, Jiawen & Meng, Lu & Zhang, Zelin & Yang, Kejia, 2023. "Low-frequency, high-impact: Discovering important rare events from UGC," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
    10. Fallahpour, Saeid & Lakvan, Eisa Norouzian & Zadeh, Mohammad Hendijani, 2017. "Using an ensemble classifier based on sequential floating forward selection for financial distress prediction problem," Journal of Retailing and Consumer Services, Elsevier, vol. 34(C), pages 159-167.
    11. Paulo Vitor Campos Souza & Luiz Carlos Bambirra Torres, 2021. "Extreme Wavelet Fast Learning Machine for Evaluation of the Default Profile on Financial Transactions," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1263-1285, April.
    12. repec:hum:wpaper:sfb649dp2016-052 is not listed on IDEAS
    13. Liao, Shu-Hsien & Yang, Ling-Ling, 2020. "Mobile payment and online to offline retail business models," Journal of Retailing and Consumer Services, Elsevier, vol. 57(C).
    14. Hazar Altinbas & Goktug Cenk Akkaya, 2017. "Improving the performance of statistical learning methods with a combined meta-heuristic for consumer credit risk assessment," Risk Management, Palgrave Macmillan, vol. 19(4), pages 255-280, November.
    15. Weidong Guo & Zach Zhizhong Zhou, 2022. "A comparative study of combining tree‐based feature selection methods and classifiers in personal loan default prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1248-1313, September.
    16. Ahmad Hammami & Mohammad Hendijani Zadeh, 2022. "Predicting earnings management through machine learning ensemble classifiers," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1639-1660, December.
    17. Saba Moradi & Farimah Mokhatab Rafiei, 2019. "A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-27, December.
    18. Lkhagvadorj Munkhdalai & Tsendsuren Munkhdalai & Oyun-Erdene Namsrai & Jong Yun Lee & Keun Ho Ryu, 2019. "An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments," Sustainability, MDPI, vol. 11(3), pages 1-23, January.
    19. Widodo, Erwin & Rochmadhan, Oryza Akbar & Lukmandono, & Januardi,, 2022. "Modeling Bayesian inspection game for non-performing loan problems," Operations Research Perspectives, Elsevier, vol. 9(C).

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