Support Vector Machine
with Information Gain Based Classification for Credit Card Fraud Detection System
Kannan Poongodi
and Dhananjay Kumar
Department of Information Technology, Anna University,
MIT Campus, Chennai, India
Abstract: In
the credit card industry, fraud is one of the major issues to handle as
sometimes the genuine credit card customers may get misclassified as fraudulent
and vice-versa. Several detection systems have been developed but the
complexity of these systems along with accuracy and precision limits its
usefulness in fraud detection applications. In this paper, a new methodology
Support Vector Machine with Information Gain (SVMIG) to improve the accuracy of
identifying the fraudulent transactions with high true positive rate for the
detection of frauds in credit card is proposed. In SVMIG, the min-max
normalization is used to normalize the attributes and the feature set of the
attributes are reduced by using information gain based attribute selection. Further,
the Apriori algorithm is used to select the frequent attribute set and to
reduce the candidate’s itemset size while detecting fraud. The experimental
results suggest that the proposed algorithm achieves 94.102% higher accuracy on
the standard dataset compared to the existing Bayesian and random forest based
approaches for a large sample size in dealing with legal and fraudulent
transactions.
Keywords: Apriori
algorithm, credit card fraud detection, information gain, support vector machine.
Received March 5, 2020; accepted September 7, 2020