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Enhancing the Classification Accuracy of Credit Default Using Extreme Gradient Boosting with Recursive Feature Selection

Publication Type : Book Chapter

Publisher : Springer, Singapore

Source : In: Kumar A., Senatore S., Gunjan V.K. (eds) ICDSMLA 2020. Lecture Notes in Electrical Engineering, vol 783. Springer, Singapore. https://doi.org/10.1007/978-981-16-3690-5_51

Url : https://link.springer.com/chapter/10.1007/978-981-16-3690-5_51

Campus : Kochi

School : School of Computing

Department : Computer Science

Year : 2021

Abstract : This research aims to propose an efficient model for the prediction of default of credit cards. The model is constructed using Extreme Gradient Boosting Ensemble technique on Taiwan based credit default dataset, which contains both financial and demographic attributes of credit card holders. The performance of the model is improved using Feature Selection and Hyperparameter Optimization methods. The result shows that the proposed model has more accuracy than many existing models for default prediction.

Cite this Research Publication : Thomas R., Vimina E.R. (November 2021) "Enhancing the Classification Accuracy of Credit Default Using Extreme Gradient Boosting with Recursive Feature Selection". In: Kumar A., Senatore S., Gunjan V.K. (eds) ICDSMLA 2020. Lecture Notes in Electrical Engineering, vol 783. Springer, Singapore. https://doi.org/10.1007/978-981-16-3690-5_51

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