Application of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier-猫眼课题宝

DOI：

10.1016/j.procs.2015.04.201

发表时间：

2015

期刊：

Procedia Computer Science

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Masoumeh Zareapoor;Pourya Shamsolmoali

随着现代科技和全球通讯高速公路的发展，信用卡诈骗案日益增多。信用卡欺诈每年给消费者和金融公司造成数十亿美元的损失，欺诈者不断试图找到新的规则和策略来实施非法行为。因此，欺诈检测系统已经成为银行和金融机构的必要条件，以最大限度地减少他们的损失。然而，由于研究人员无法获得信用卡交易数据集，因此缺乏关于信用卡欺诈检测技术的公开文献。最常用的欺诈检测方法是朴素贝叶斯（NB），支持向量机（SVM），K-最近邻算法（KNN）。这些技术可以单独使用，也可以使用集成或元学习技术来构建分类器。但在所有现有的方法中，集成学习方法被认为是流行和常见的方法，不是因为它非常简单的实现，而是因为它在实际问题上具有出色的预测性能。在本文中，我们训练了各种数据挖掘技术用于信用卡欺诈检测和评估每个方法的基础上一定的设计标准。经过多次试验和比较，我们引入了基于决策三的Bagging分类器作为最佳分类器，

Credit card fraud is increasing considerably with the development of modern technology and the global superhighways of communication. Credit card fraud costs consumers and the financial company billions of dollars annually, and fraudsters continuously try to find new rules and tactics to commit illegal actions. Thus, fraud detection systems have become essential for banks and financial institution, to minimize their losses. However, there is a lack of published literature on credit card fraud detection techniques, due to the unavailable credit card transactions dataset for researchers. The most commonly techniques used fraud detection methods are Naïve Bayes (NB), Support Vector Machines (SVM), K-Nearest Neighbor algorithms (KNN). These techniques can be used alone or in collaboration using ensemble or meta-learning techniques to build classifiers. But amongst all existing method, ensemble learning methods are identified as popular and common method, not because of its quite straightforward implementation, but also due to its exceptional predictive performance on practical problems. In this paper we trained various data mining techniques used in credit card fraud detection and evaluate each methodology based on certain design criteria. After several trial and comparisons; we introduced the bagging classifier based on decision three, as the best classifier to

Application of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier

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