Outlier Detection Using Gaussian Mixture Model Clustering to Optimize XGBoost for Credit Approval Prediction

Setiadi ; De Rosal Ignatius Moses ; Muslikh ; Ahmad Rofiqul ; Iriananda ; Syahroni Wahyu ; Warto ; Warto ; Gondohanindijo ; Jutono ; Ojugo ; Arnold Adimabua

Journal of Computing Theories and Applications 📅 2024 Vol 2, No 2 🔗 DOI: 10.62411/jcta.11638

Abstract

Credit approval prediction is one of the critical challenges in the financial industry, where the accuracy and efficiency of credit decision-making can significantly affect business risk. This study proposes an outlier detection method using the Gaussian Mixture Model (GMM) combined with Extreme Gradient Boosting (XGBoost) to improve prediction accuracy. GMM is used to detect outliers with a probabilistic approach, allowing for finer-grained anomaly identification compared to distance- or density-based methods. Furthermore, the data cleaned through GMM is processed using XGBoost, a decision tree-based boosting algorithm that efficiently handles complex datasets. This study compares the performance of XGBoost with various outlier detection methods, such as LOF, CBLOF, DBSCAN, IF, and K-Means, as well as various other classification algorithms based on machine learning and deep learning. Experimental results show that the combination of GMM and XGBoost provides the best performance with an accuracy of 95.493%, a recall of 91.650%, and an AUC of 95.145%, outperforming other models in the context of credit approval prediction on an imbalanced dataset. The proposed method has been proven to reduce prediction errors and improve the model's reliability in detecting eligible credit applications.

Keywords

Credit Approval Prediction; Data Preprocessing; Ensemble Learning; Gaussian Mixture Model; Imbalanced Data; Outlier Clustering; Outlier Detection; XGBoost

How to Cite

Setiadi, et al. (2024). Outlier Detection Using Gaussian Mixture Model Clustering to Optimize XGBoost for Credit Approval Prediction. Journal of Computing Theories and Applications, 2(2). https://doi.org/10.62411/jcta.11638

Setiadi, De Rosal Ignatius Moses; Muslikh, Ahmad Rofiqul; Iriananda, Syahroni Wahyu; Warto, Warto; Gondohanindijo, Jutono; Ojugo, Arnold Adimabua, "Outlier Detection Using Gaussian Mixture Model Clustering to Optimize XGBoost for Credit Approval Prediction," Journal of Computing Theories and Applications, vol. 2, no. 2, 2024.

Setiadi, De Rosal Ignatius Moses; Muslikh, Ahmad Rofiqul; Iriananda, Syahroni Wahyu; Warto, Warto; Gondohanindijo, Jutono; Ojugo, Arnold Adimabua. "Outlier Detection Using Gaussian Mixture Model Clustering to Optimize XGBoost for Credit Approval Prediction." Journal of Computing Theories and Applications, vol. 2, no. 2, 2024.

Setiadi, et al. (2024) 'Outlier Detection Using Gaussian Mixture Model Clustering to Optimize XGBoost for Credit Approval Prediction', Journal of Computing Theories and Applications, 2(2). doi: 10.62411/jcta.11638.

Setiadi, De Rosal Ignatius Moses; Muslikh, Ahmad Rofiqul; Iriananda, Syahroni Wahyu; Warto, Warto; Gondohanindijo, Jutono; Ojugo, Arnold Adimabua. Outlier Detection Using Gaussian Mixture Model Clustering to Optimize XGBoost for Credit Approval Prediction. Journal of Computing Theories and Applications. 2024;2(2).

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