@Article{10798587.2017.1321228,
AUTHOR = {Masoumeh Zareapoor, Jie Yang},
TITLE = {A Novel Strategy for Mining Highly Imbalanced Data in Credit Card Transactions},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {24},
YEAR = {2018},
NUMBER = {4},
PAGES = {721--727},
URL = {http://www.techscience.com/iasc/v24n4/39798},
ISSN = {2326-005X},
ABSTRACT = {The design of an efficient credit card fraud detection technique is, however, particularly challenging, 
due to the most striking characteristics which are; imbalancedness and non-stationary environment 
of the data. These issues in credit card datasets limit the machine learning algorithm to show a 
good performance in detecting the frauds. The research in the area of credit card fraud detection 
focused on detection the fraudulent transaction by analysis of normality and abnormality concepts. 
Balancing strategy which is designed in this paper can facilitate classification and retrieval problems 
in this domain. In this paper, we consider the classification problem in supervised learning scenario 
by creating a contrast vector for each customer based on its historical behaviors. The performance 
evaluation of proposed model is made possible by a real credit card data-set provided by FICO, and it 
is found that the proposed model has significant performance than other state-of-the-art classifiers.},
DOI = {10.1080/10798587.2017.1321228}
}