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The Frontiers of Society, Science and Technology, 2020, 2(11); doi: 10.25236/FSST.2020.021115.

Efficient Fraud Detection Classification: Class Imbalance and Attribute Correlations


Difei Liu1, Ruiyi Sun2, Haoyang Ren3

Corresponding Author:
Difei Liu

1 University of Warwick, Coventry, CV4 7AL, UK

2 Australian National University Canberra, ACT, 0200

3 Beijing Normal University, Zhuhai, China


Fraud detection is a specifically important issue to protect cardholders’ information from being stolen by fraudsters. By choosing proper algorithms and analyzing behavioural information of cardholders and banks, we can significantly reduce the probability of transactions being illegally manipulated. In response to possible problems in fraud analysis, this article will focus especially on tackling class imbalance problems and finding attribute correlations. Two FraudDetection datasets on Kaggle will be used to build classifiers and ananlyze the impact of different data processing techniques. Through this process, we realized recent findings of fraud detection, we got to know more about different data processing methods, and we implemented distinct types of classifiers. We confirmed the significance of class imbalance tackling and attribute correlations analyzing.


Fraud detection, Class imbalance, Attribute correlations, Classification algorithms

Cite This Paper

Difei Liu, Ruiyi Sun, Haoyang Ren. Efficient Fraud Detection Classification: Class Imbalance and Attribute Correlations. The Frontiers of Society, Science and Technology (2020) Vol. 2 Issue 11: 96-103. https://doi.org/10.25236/FSST.2020.021115.


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