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All Journal Journal of Soft Computing Exploration
Kamilah Ahmad
Universiti Tun Hussein Onn Malaysia, Malaysia
Computer Science & IT

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Using genetic algorithm feature selection to optimize XGBoost performance in Australian credit Dwika Ananda Agustina Pertiwi; Kamilah Ahmad; Shahrul Nizam Salahudin; Ahmed Mohamed Annegrat; Much Aziz Muslim
Journal of Soft Computing Exploration Vol. 5 No. 1 (2024): March 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i1.302

Abstract

To reduce credit risk in credit institutions, credit risk management practices need to be implemented so that lending institutions can survive in the long term. Data mining is one of the techniques used for credit risk management. Where data mining can find information patterns from big data using classification techniques with the resulting level of accuracy. This research aims to increase the accuracy of classification algorithms in predicting credit risk by applying genetic algorithms as the best feature selection method. Thus, the most important feature will be used to search for credit risk information. This research applies a classification method using the XGBoost classifier on the Australian credit dataset, then carries out an evaluation by measuring the level of accuracy and AUC. The results show an increase in accuracy of 2.24%, with an accuracy value of 89.93% after optimization using a genetic algorithm. So, through research on genetic algorithm feature selection, we can improve the accuracy performance of the XGBoost algorithm on the Australian credit dataset.
Co-Authors Ahmed Mohamed Annegrat Dwika Ananda Agustina Pertiwi Much Aziz Muslim Shahrul Nizam Salahudin