Autosoft Journal

Online Manuscript Access

A novel strategy for mining highly imbalanced data in credit card transactions



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.



Total Pages: 7
Pages: 721-727


Manuscript ViewPdf Subscription required to access this document

Obtain access this manuscript in one of the following ways

Already subscribed?

Need information on obtaining a subscription? Personal and institutional subscriptions are available.

Already an author? Have access via email address?


Volume: 24
Issue: 4
Year: 2018

Cite this document


Ali A. International Journal of Advances in Soft Computing & its Applications

Chawla N.V. Journal of Artificial Intelligent

Chen, Sheng, and Haibo He. "Towards Incremental Learning of Nonstationary Imbalanced Data Stream: a Multiple Selectively Recursive Approach." Evolving Systems 2.1 (2010): 35-50. Crossref. Web.

Dal Pozzolo, Andrea et al. "Learned Lessons in Credit Card Fraud Detection from a Practitioner Perspective." Expert Systems with Applications 41.10 (2014): 4915-4928. Crossref. Web.

Appice, Annalisa et al., eds. "Machine Learning and Knowledge Discovery in Databases." Lecture Notes in Computer Science (2015): n. pag. Crossref. Web.

Dong, Aimei, Fu-lai Chung, and Shitong Wang. "Semi-Supervised Classification Method through Oversampling and Common Hidden Space." Information Sciences 349-350 (2016): 216-228. Crossref. Web.

ACM Computing Surveys 46.4 (2014): n. pag. Crossref. Web.

García, Salvador, Julián Luengo, and Francisco Herrera. "Tutorial on Practical Tips of the Most Influential Data Preprocessing Algorithms in Data Mining." Knowledge-Based Systems 98 (2016): 1-29. Crossref. Web.

Han, Fengqing et al. "New Support Vector Machine for Imbalance Data Classification." Intelligent Automation & Soft Computing 18.6 (2012): 679-686. Crossref. Web.

Haibo He, and E.A. Garcia. "Learning from Imbalanced Data." IEEE Transactions on Knowledge and Data Engineering 21.9 (2009): 1263-1284. Crossref. Web.

Japkowicz N. Intelligent data analysis

Kolter J.Z. Journal of machine learning research

Xu-Ying Liu, Jianxin Wu, and Zhi-Hua Zhou. "Exploratory Undersampling for Class-Imbalance Learning." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39.2 (2009): 539-550. Crossref. Web.

Nekooeimehr, Iman, and Susana K. Lai-Yuen. "Adaptive Semi-Unsupervised Weighted Oversampling (A-SUWO) for Imbalanced Datasets." Expert Systems with Applications 46 (2016): 405-416. Crossref. Web.

Nian, Ke et al. "Auto Insurance Fraud Detection Using Unsupervised Spectral Ranking for Anomaly." The Journal of Finance and Data Science 2.1 (2016): 58-75. Crossref. Web.

ACM SIGKDD Explorations Newsletter 6.1 (2004): n. pag. Crossref. Web.

Quah, Jon T.S., and M. Sriganesh. "Real-Time Credit Card Fraud Detection Using Computational Intelligence." Expert Systems with Applications 35.4 (2008): 1721-1732. Crossref. Web.

Sun, Zhongbin et al. "A Novel Ensemble Method for Classifying Imbalanced Data." Pattern Recognition 48.5 (2015): 1623-1637. Crossref. Web.

Sundarkumar, G. Ganesh, and Vadlamani Ravi. "A Novel Hybrid Undersampling Method for Mining Unbalanced Datasets in Banking and Insurance." Engineering Applications of Artificial Intelligence 37 (2015): 368-377. Crossref. Web.

Van Vlasselaer, Véronique et al. "APATE : A Novel Approach for Automated Credit Card Transaction Fraud Detection Using Network-Based Extensions." Decision Support Systems 75 (2015): 38-48. Crossref. Web.

West, Jarrod, and Maumita Bhattacharya. "Intelligent Financial Fraud Detection: A Comprehensive Review." Computers & Security 57 (2016): 47-66. Crossref. Web.

Weston, David J. et al. "Plastic Card Fraud Detection Using Peer Group Analysis." Advances in Data Analysis and Classification 2.1 (2008): 45-62. Crossref. Web.

Information Systems Journal 22.1 (2012): n. pag. Crossref. Web.

Leung, Chi Sing, Minho Lee, and Jonathan H. Chan, eds. "Neural Information Processing." Lecture Notes in Computer Science (2009): n. pag. Crossref. Web.

Zhang, Zhongliang et al. "Empowering One-Vs-One Decomposition with Ensemble Learning for Multi-Class Imbalanced Data." Knowledge-Based Systems 106 (2016): 251-263. Crossref. Web.


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present


TSI Press
18015 Bullis Hill
San Antonio, TX 78258 USA
PH: 210 479 1022
FAX: 210 479 1048