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A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost

Computers, Materials & Continua 2023年第3期74卷 5951-5965页

作者： Wang Ning Siliang Chen Fu Qiang Haitao Tang Shen Jie College of Computer and Communication Hunan Institute of EngineeringXiangtan411104China College of Computational Science and Electronics Hunan Institute of EngineeringXiangtan411104China

With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detection has started to use this method in large numbers, but thetraditional Adaboost is prone to overfitting in the presence of noisy ***, in order to alleviate this phenomenon, this paper proposes a newidea: using the number of consecutive sample misclassifications to determinethe noisy samples, while constructing a penalty factor to reconstruct thesample weight assignment. Firstly, the theoretical analysis shows that thetraditional Adaboost method is overfitting in a noisy training set, which leadsto the degradation of classification accuracy. To this end, the penalty factorconstructed by the number of consecutive misclassifications of samples isused to reconstruct the sample weight assignment to prevent the classifierfrom over-focusing on noisy samples, and its reasonableness is ***, by comparing the penalty strength of the three different penalty factorsproposed in this paper, a more reasonable penalty factor is ***, in order to make the constructed model more in line with theactual requirements on training time consumption, the Adaboost algorithmwith adaptive weight trimming (AWTAdaboost) is used in this paper, so thepenalty factor-based AWTAdaboost (PF_AWTAdaboost) is finally ***, PF_AWTAdaboost is experimentally validated against other traditionalmachine learning algorithms on credit card fraud datasets and otherdatasets. The results show that the PF_AWTAdaboost method has betterperformance, including detection accuracy, model recall and robustness, thanother methods on the credit card fraud dataset. And the PF_AWTAdaboostmethod also shows excellent generalization performance on other *** the experimental results, it is shown that the PF_AWTAdaboost algorithmhas better classification

关键词： Credit card fraud noisy samples penalty factors AWTadaboost algorithm

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