Online Transaction Detection Method Using Catboost Model

Yunlong Li, Yingan Mai, Zijian Lin, Shufen Liang
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引用次数: 2

Abstract

The blossom of online transaction has given rise to fraud, especially credit card fraud. Thus, Fraud Detection algorithm has become a critical issue, and several attempts have been made to detect transaction fraud using data mining methods. However, these methods suffer from lack of data or inadequate feature engineering. In this paper, we applied sufficient feature engineering and proposed a fraud detection algorithm based on Catboost. The experimental result indicates that our model outperforms other classical models, such as Logistic Regression, Support Vector Machine, and Random Forest. Moreover, we also illustrate the feature importance, which is valuable for feature selection and performance tuning.
基于Catboost模型的在线交易检测方法
网上交易的蓬勃发展导致了欺诈,尤其是信用卡欺诈。因此,欺诈检测算法已经成为一个关键问题,人们已经尝试使用数据挖掘方法来检测交易欺诈。然而,这些方法受到缺乏数据或不充分的特征工程的影响。本文运用充分的特征工程,提出了一种基于Catboost的欺诈检测算法。实验结果表明,我们的模型优于其他经典模型,如逻辑回归、支持向量机和随机森林。此外,我们还说明了特征的重要性,这对特征选择和性能调优有价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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