As the number of credit card users has increased, detecting fraud in this domain has become a vital issue. Previous literature has applied various supervised and unsupervised machine learning methods to find an effective fraud detection system. However, some of these methods require an enormous amount of time to achieve reasonable accuracy. In this paper, an Asexual Reproduction Optimization (ARO) approach was employed, which is a supervised method to detect credit card fraud. ARO refers to a kind of production in which one parent produces some offspring. By applying this method and sampling just from the majority class, the effectiveness of the classification is increased. A comparison to Artificial Immune Systems (AIS), which is one of the best methods implemented on current datasets, has shown that the proposed method is able to remarkably reduce the required training time and at the same time increase the recall that is important in fraud detection problems. The obtained results show that ARO achieves the best cost in a short time, and consequently, it can be considered a real-time fraud detection system.

翻译：随着信用卡用户数量的增加，该领域的欺诈检测已成为一个关键问题。已有文献采用多种有监督和无监督机器学习方法来构建有效的欺诈检测系统，然而其中一些方法需要大量时间才能获得合理精度。本文采用了一种名为无性繁殖优化（ARO）的有监督方法进行信用卡欺诈检测。ARO是一种亲代个体产生若干子代个体的繁殖方式。通过应用该方法并仅对多数类进行采样，分类的有效性得以提升。与当前数据集上表现最佳的人工免疫系统（AIS）方法相比，该方法能显著缩短训练时间，同时提高欺诈检测问题中至关重要的召回率。实验结果表明，ARO能在短时间内达到最优成本，因此可被视为一种实时欺诈检测系统。