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Analysis of Credit Card Fraud Detection Using Fusion Classifiers

  • Priyanka KumariEmail author
  • Smita Prava Mishra
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 711)

Abstract

Credit card fraud detection is a critical problem that has been faced by online vendors at the finance marketplace every now and then. The rapid and fast growth of the modern technologies causes the fraud and heavy financial losses for many financial sectors. Different data mining and soft computing-based classification algorithms have been used by most of the researchers, and it plays an essential role in fraud detection. In this paper, we have analyzed some ensemble classifiers such as Bagging, Random Forest, Classification via Regression, Voting and compared them with some effective single classifiers like K-NN, Naïve Bayes, SVM, RBF Classifier, MLP, Decision Tree. The evaluation of these algorithms is carried out through three different datasets and treated with SMOTE, to deal with the class imbalance problem. The comparison is based on some evaluation metrics like accuracy, precision, true positive rate or recall, and false positive rate.

Keywords

Credit card fraud Fraud detection SMOTE Cross-validation MLP Bagging Voting Random forest Classification via regression 

Notes

Acknowledgements

I would like to express my heartfelt thanks to Prof. Dr. Debahuti Mishra, Head of the Dept (Computer Science and Engineering) at ITER, for her encouragements and support throughout the work.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Computer Science and Engineering, ITERSOA UniversityBhubaneswarIndia
  2. 2.Computer Science and Information Technology, ITERSOA UniversityBhubaneswarIndia

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