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.

本文介绍了一个基于有性繁殖优化方法，即 ARO，结合机器学习实现的信用卡欺诈检测系统，相较其他方法，ARO 方法大大降低了训练时间，提高了欺诈检测中的召回率，可用于实时系统。