Comprehensive Analysis of Resampling Methods on Ensemble Learning for Credit Card Fraud Detection
نویسندگان
چکیده
Kredi kartı aracılığıyla hızlı ve kolay satın alma işlemleri sahtecilik işlemlerinin artmasına neden olmuştur. Son yıllarda makine öğrenmesi yöntemlerinin kullanımı tespiti işlemlerinde önemli bir pay oluşturmuştur. Sahtecilik 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 yöntemleri olmak üzere çeşitli yöntemlerini kullanarak metotlarının aşamalara göre yarattığı etkileri karşılaştırmaktadır. Karşılaştırma sonucunda, çapraz doğrulama metodu eğitim test kümelerine ayrı yapılmasının en doğru sonucu verdiği gösterilmiştir. Bununla birlikte bu çalışmada XGB, LGBM, RF, FNN diğer metotların metrik değerlerine dayanan başka kıyaslamada ise XGB %99 duyarlılık, kesinlik doğruluk ile yüksek değerlere ulaşmışlardır.
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ژورنال
عنوان ژورنال: Fen ve mühendislik bilimleri dergisi
سال: 2022
ISSN: ['2147-5296', '2149-3367']
DOI: https://doi.org/10.35414/akufemubid.1066453