Spurious credit card transactions are a significant source of financial losses and urge the development of accurate fraud detection algorithms. In this paper, we use machine learning strategies for such an aim. First, we apply a mixed learning technique that uses K-means preprocessing before trained classification to the problem at hand. Next, we introduce an adapted detector ensemble technique that uses OR-logic algorithm aggregation to enhance the detection rate. Then, both strategies are deployed in tandem in numerical simulations using real-world transactions data. We observed from simulation results that the proposed methods diminished computational cost and enhanced performance concerning state-of-the-art techniques.

翻译：净化信用卡交易是金融损失的重要来源,敦促开发准确的欺诈检测算法。 在本文中,我们为此使用机器学习策略。 首先,我们采用混合学习技术,在经过培训的分类之前先使用K手段进行预处理,然后对手头的问题进行分类。接下来,我们采用经调整的检测器混合技术,使用OR-逻辑算法汇总来提高检测率。然后,这两种战略在数字模拟中同时运用,使用真实世界交易数据。我们从模拟结果中观察到,拟议方法降低了计算成本,提高了最新技术的性能。