Comprehensive Analysis of Resampling Methods on Ensemble Learning for Credit Card Fraud Detection

Year 2022, Volume: 22 Issue: 5, 1005 - 1015, 27.10.2022

Ali Kemal Ay Esra Yolaçan

https://doi.org/10.35414/akufemubid.1066453

Cited By: 1

Abstract

Rapid and easy purchases via credit cards have led to a rise in fraudulent transactions. In recent years, machine learning methods have been an important part of fraud detection processes. One of the common problems encountered in processes of fraud detection is the imbalance in datasets. Resampling methods used for the problem of imbalance may differ from study to study in terms of the stages these methods are applied. This study compares the effects of resampling methods according to these stages, using various ensemble learning methods, including a few machine learning and deep learning methods. The comparison utilizing cross-validation technique shows that applying the resampling methods separately to the training and test datasets method gives the most accurate result. However, in another comparison by metric scores of XGB, LGBM, RF, FNN, and other methods used in this study, XGB and FNN techniques give the highest values with 99% recall, precision, and accuracy.

Keywords

Imbalanced Dataset, Resampling Methods, Ensemble Learning, Credit Card Fraud Detection, Machine Learning

Yeniden Örnekleme Metotlarının Kredi Kartı Sahtecilik Tespiti için Topluluk Öğrenmesine Kapsamlı Analizi

Abstract

Kredi kartı aracılığıyla hızlı ve kolay satın alma işlemleri sahtecilik işlemlerinin artmasına neden olmuştur. Son yıllarda makine öğrenmesi yöntemlerinin kullanımı sahtecilik tespiti işlemlerinde önemli bir pay oluşturmuştur. Sahtecilik tespiti işlemlerinde karşılaşılan yaygın problemlerden birisi veri kümelerinin dengesiz olmasıdır. Dengesizlik problemi için kullanılan yeniden örnekleme metotları kullanıldıkları aşamalar bakımından çalışmadan çalışmaya farklılık gösterebilmektedir. Bu çalışma başlıca topluluk öğrenmesi yöntemleri olmak üzere çeşitli makine öğrenmesi yöntemlerini kullanarak yeniden örnekleme metotlarının kullanıldıkları aşamalara göre yarattığı etkileri karşılaştırmaktadır. Karşılaştırma sonucunda, çapraz doğrulama metodu aracılığıyla yeniden örnekleme metotlarının eğitim ve test veri kümelerine ayrı ayrı yapılmasının en doğru sonucu verdiği gösterilmiştir. Bununla birlikte bu çalışmada kullanılan XGB, LGBM, RF, FNN ve diğer metotların metrik değerlerine dayanan bir başka kıyaslamada ise XGB ve FNN metotları %99 duyarlılık, kesinlik ve doğruluk ile en yüksek değerlere ulaşmışlardır.

Keywords

Dengesiz Veri Kümesi, Yeniden Örnekleme Metotları, Topluluk Öğrenmesi, Kredi Kartı Sahtecilik Tespiti, Makine Öğrenmesi

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