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A machine learning-based credit risk prediction engine system using a stacked classifier and a filter-based feature selection method | Journal of Big Data



  • Moradi S, Mokhatab RF. A dynamic credit risk assess- ment model with data mining techniques: evidence from Iranian banks. Financ Innov. 2019;5(1):15.

    Article 

    Google Scholar
     

  • Rehman ZU, Muhammad N, Sarwar B, Raz MA. Impact of risk management strategies on the credit risk faced by commercial banks of Balochistan. Financ Innov. 2019;5(1):44.

    Article 

    Google Scholar
     

  • Khemakhem S, Boujelbene Y. Predicting credit risk on the basis of financial and non-financial variables and data mining. Rev Acc Financ. 2018;17(3):316–40.

    Article 

    Google Scholar
     

  • Dornadula VN, Geetha S. Credit card fraud detection using machine learning algorithms. Procedia Computer Science. 2019;165:631–41.

    Article 

    Google Scholar
     

  • Garcıa V, Marques AI, S´anchez J.S. Improving Risk Pre- dictions by Preprocessing Imbalanced Credit Data. Neural Information Processing. 2012;67:68–75.


    Google Scholar
     

  • Song Y, Peng Y. A MCDM-Based Evaluation Approach for Imbalanced Classification Methods in Financial Risk Prediction. IEEE Access. 2019;7:84897–906.

    Article 

    Google Scholar
     

  • Guo S, He H, Huang X. A multi-stage self-adaptive classi- fier ensemble model with application in credit scoring. IEEE Access. 2019;7:78549–59.

    Article 

    Google Scholar
     

  • Liu H, Yu L. Toward integrating feature selection algorithms for classification and clustering. IEEE Tran Knowl Data Eng. 2005;17(4):491–502.

    Article 

    Google Scholar
     

  • Tang PS, Tang XL, Tao ZY, Li JP (2014) Research on feature selection algorithm based on mutual information and genetic algorithm. 11th Int. Comput. Conf. Wavelet Active Media Tech. Inf. Processing (ICCWAMTIP) IEEE, 403–406.

  • Liu C, Wang Q, Zhao Q, Shen X, Konan M. A new feature selection method based on a validity index of feature subset. Pattern Recogn Lett. 2017;92:1–8.

    Article 

    Google Scholar
     

  • Pandey TN, Jagadev AK, Mohapatra SK, Dehuri S (2017) Credit risk analysis using machine learning classifiers. In: 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) (pp. 1850–1854). IEEE.

  • Zhang L, Hui X, Wang L (2009) Application of adaptive support vector machines method in credit scoring. In: International Conference on Management Science and Engineering, 1410–1415.

  • Mohammadi N, Zangeneh M. Customer credit risk assess- ment using artificial neural networks. IJ Information Technol Computer Science. 2016;8(3):58–66.


    Google Scholar
     

  • Hsu TC, Liou ST, Wang YP, Huang YS, Che-Lin (2019) Enhanced Recurrent Neural Network for Combining Static and Dynamic Features for Credit Card Default Prediction. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1572–1576.

  • Bao W, Lianju N, Yue K. Integration of unsupervised and supervised machine learning algorithms for credit risk assessment. Expert Syst Appl. 2019;128:301–15.

    Article 

    Google Scholar
     

  • Ha VS, Lu DN, Choi GS, Nguyen HN, Yoon B (2019) Improv- ing credit risk prediction in online peer-to-peer (P2P) lending using feature selection with deep learning. In: 21st International Conference on Advanced Communication Technology, 511–515.

  • Chen C, Zhang Q, Yu B, Yu Z, Lawrence PJ, Ma Q, Zhang Y. Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier. Comput Biol Med. 2020;123: 103899.

    Article 

    Google Scholar
     

  • Chakrabarty N, Kundu T, Dandapat S, Sarkar A, Kole DK (2019) Flight arrival delay prediction using gradient boosting classifier. In: Emerging technologies in data mining and information security, 651-659

  • Weldegebriel HT, Liu H, Haq AU, Bugingo E, Zhang D. A new hybrid convolutional neural network and eXtreme gradient boosting classifier for recognizing handwritten Ethiopian characters. IEEE Access. 2019;8:17804–18.

    Article 

    Google Scholar
     

  • Liang J, Qin Z, Xiao S, Ou L, Lin X. Efficient & secure decision tree classification for cloud-assisted online diagnosis services. IEEE Trans Dependable Secure Comput. 2019;18(4):1632–44.

    Article 

    Google Scholar
     

  • Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.

    Article 

    Google Scholar
     

  • Trstenjak B, Mikac S, Donko D. KNN with TF-IDF based framework for text categorization. Procedia Eng. 2014;69:1356–64.

    Article 

    Google Scholar
     

  • Tan S. An effective refinement strategy for KNN text classifier. Expert Syst Appl. 2006;3(2):290–8.

    Article 

    Google Scholar
     

  • Kasongo SM, Sun Y. A deep learning method with filter based feature engineering for wireless intrusion detection system. IEEE access. 2019;7:38597–607.

    Article 

    Google Scholar
     

  • “UCI Machine Learning Repository: Stat-log (Australian Credit Approval) DataSet.” http://archive.ics.uci.edu/ml/datasets/statlog+(australian+credit+approval) (accessed Oct. 31, 2020).

  • “UCI Machine Learning Repository: Stat-log (German Credit Data) Data Set.” https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data) (accessed Oct. 31, 2020).

  • “UCI Machine Learning Repository: default of credit card clients Data Set.” https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients (accessed Mar. 14, 2020).

  • Gao Z, Xu Y, Meng F, Qi F, Lin Z (2014) Improved information gain-based feature selection for text categorization. Int. Conf. Wireless Commun. Vehicular Technol. Inform Theory and Aerosp. Electron. Sys. (VITAE) IEEE, 1–5.

  • Shannon CE. A mathematical theory of communication. ACM SIGMOBILE. 2001;5(1):3–55.

    MathSciNet 

    Google Scholar
     

  • Zhou H, Deng Z, Xia Y, Fu M. A new sampling method in particle filter based on pearson correlation coefficient. Neurocomputing. 2016;216:208–15.

    Article 

    Google Scholar
     

  • Google Colab [Online]. Available: https://colab.research.google.com/

  • Scikit-learn : machine learning in Python. https://scikit-learn.org/stable/

  • Ileberi E, Sun Y, Wang Z. A machine learning based credit card fraud detection using the GA algorithm for feature selection. J Big Data. 2022;9:24.

    Article 

    Google Scholar
     

  • Lipton ZC, Elkan C, Narayanaswamy B (2014) Thresh- olding Classifiers to Maximize F1 Score. arXiv:1402.1892 [cs, stat], May 2014, Accessed: Nov. 01, 2020. http://arxiv.org/abs/1402.1892

  • Muschelli J. ROC and AUC with a binary predictor: a poten- tially misleading metric. J Classif. 2020;37(3):696–708.

    Article 
    MathSciNet 

    Google Scholar
     

  • Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA. Generative adversarial networks: An overview. IEEE Signal Process Mag. 2018;35(1):53–65.

    Article 

    Google Scholar
     

  • Zhao T, Zheng Y, Wu Z. Feature selection-based machine learning modeling for distributed model predictive control of nonlinear processes. Computers Chem Eng. 2023;169:108074.

    Article 

    Google Scholar
     

  • Edmond C, Girsang AS. Classification performance for credit scoring using neural network. Int J. 2020;2020(8):5.


    Google Scholar
     

  • Laudani A, Lozito GM, Fulginei FR, Salvini A. On training efficiency and computational costs of a feed forward neural network: A review. Comput Intell Neurosci. 2015;2015(2015):83.


    Google Scholar
     

  • Stoffel M, Bamer F, Markert B. (2019). Stability of feed forward artificial neural networks versus nonlinear structural models in high speed deformations: A critical comparison. Arch Mech. 2019;71(2):34



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