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Application of SIRUS in Credit Card Fraud Detection

  • Yuwei Zhang
  • Guanjun LiuEmail author
  • Wenjing Luan
  • Chungang Yan
  • Changjun Jiang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11280)

Abstract

Credit card fraud problem is very common in recent years. It not only causes economic loss to people, but also causes trust crisis to enterprises. Due to the imbalance of data, fraud detection has always been tricky. In our previous work, we proposed a method of dealing with the class imbalance problem based on stacking ensemble learning and inverse random undersampling method (SIRUS). First, the inverse random undersampling method is used to generate multiple data subsets from the original data set. Then we use the stacking ensemble learning method for each data subset to train several different learners (also called first-level learners), and then the results generated by each first-level learner are taken as features to train a meta learner. We apply SIRUS to detect the credit card fraud in this paper. Our dataset comes from a financial company in China. A variety of measurements such as recall, precision, accuracy, F-measure, and G-mean to illustrate the effectiveness of our method in fraud detection.

Keywords

Fraud detection Stacking Inverse random undersampling Class imbalance problem 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yuwei Zhang
    • 1
    • 2
  • Guanjun Liu
    • 1
    • 2
    Email author
  • Wenjing Luan
    • 1
    • 2
  • Chungang Yan
    • 1
    • 2
  • Changjun Jiang
    • 1
    • 2
  1. 1.Department of Computer Science and TechnologyTongji UniversityShanghaiChina
  2. 2.Key Laboratory of Embedded System and Service ComputingMinistry of EducationShanghaiChina

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