Probabilistic Support Vector Regression Classification Model for Credit Card Fraud Detection

Authors

  • Sawhney N Dept. of Information Technology, Chandigarh Engineering College, Mohali, India
  • Kaur B Dept. of Information Technology, Chandigarh Engineering College, Mohali, India
  • Kaur H SGGS collegiate public school, Mohali, India

DOI:

https://doi.org/10.26438/ijcse/v6i9.840843

Keywords:

Credit card fraud detection, Early Fraud Detection, Regressive analysis, Linear regression models

Abstract

The researchers have already worked with many supervised and unsupervised methods for the purpose of credit card fraud detection. The supervised models have been found more efficient for the purpose of credit card fraud detection. The major goal of the credit card fraud detection research is to improve the accuracy while decreasing the elapsed time. The proposed credit card fraud detection models purposes the use of feature extraction and selection of the credit card data with linear re-gression algorithm for the credit card fraud detection. The feature engineering and analysis would be performed over the giv-en transactional data and then final classification of the anomalies or outliers is done using linear regression classifier. The proposed model has been tested under the various experiments from the various groups of test cases. The test case groups have been obtained after applying the various levels of the feature elimination and feature selection over the collection of credit card transaction data. The proposed credit card fraud classification model is based upon two different models, which includes the Naïve Bayes and Support Vector Regression. The main aim of the research is to achieve the higher credit card fraud recognition accuracy, with the minimum classification complexity.

References

[1] Li, Jinyan, Simon Fong, Yan Zhuang, and Richard Khoury. "Hierarchical classification in pattern discovery for outlier analysis of credit card database." Soft Computing (2015): 1-10.

[2] Prollochs, Nicolas, Stefan Feuerriegel, and Dirk Neumann. "Enhancing Outlier analysis of Financial News by Detecting Negation Scopes." InSystem Sciences (HICSS), 2015 48th Ha-waii International Conference on, pp. 959-968. IEEE, 2015.

[3] Cui, Limeng, Fan Meng, Yong Shi, Minqiang Li, and An Liu. "A Hierarchy Method Based on LDA and SVM for Credit card fraud detection." In Data Mining Workshop (ICDMW), 2014 IEEE International Conference on, pp. 60-64. IEEE, 2014.

[4] Ouyang, Yuanxin, Yao Huangfu, Hao Sheng, and Zhang Xiong. " target on the problem of news title classification which is an essential and typical member in short text family." In Neural Information Processing, pp. 581-588. Springer Inter-national Publishing, 2014.

[5] Krishnlal G, S Babu Rengarajan, K G Srinivasagan, “ A new pattern discovery approach based on HMM-SVM for web credit card fraud detection” International Journal of Computer Applications (0975-8887) Volumn 1- No.19,2010.

[6] Vandana Korde, C namrata Mahender, “Text classification and classifier a survey,” International Journal of Artificial In-telligence and Application (IJAIA), vol.3, No.2, March2012.

[7] Mita K. Dalal, Mukesh A.Zaveri,” Automatic text Classifica-tion,” International Journal of Computer Applications (0975-8887) Volumn 28- No.2, August 2011.

[8] Denial I.Morariu, Lucian N. Vintan, and Volker Tresp, “Meta- Clalssification using SVM classifiers for text documents “, “World Academy of science engineering and technology” 21,2006.

[9] Hyeran Byun 1 and Seong-Whan Lee2, “ Application of Sup-port Vector machines for pattern recognition: A Survey,” SVM 2002, LNCS 2388,pp.213-236,2002.

[10] D. Morariu, R. Cre,Tulescu and L.,Vin,tan, “ improving the SVM Meta Clssifier for text document by using Naïve bayes,”Int. J. of Computers, communication and control, ISSN 1841-9844.

[11] Lie Lu, Stan Z. Li and Hong –Jiang Zhang, “ Content based Audion Segmentation using Support vector machine.”

[12] CREŢULESCU, Radu George, and N. VINŢAN Lucian. "Con-tributions to Document Classification System Design." Vol. 1, Issue 1, SIBIU, 2011.

[13] Hyeran Byun 1 and Seong-Whan Lee2, “ Application of Sup-port Vector machines for pattern recognition: A Survey,” SVM 2002, LNCS 2388,pp.213-236,2002.

[14] Krishnlal G, S Babu Rengarajan, K G Srinivasagan, “ A new pattern discovery approach based on HMM-SVM for web credit card fraud detection” International Journal of Computer Applications (0975-8887) Volumn 1- No.19,2010.

[15] Zhang, Yulei, Yan Dang, Hsinchun Chen, Mark Thurmond, and Cathy Larson. "Automatic credit card database monitoring and classification for syndromic surveillance." Decision Sup-port Systems 47, no. 4 (2009): 508-517.

Downloads

Published

2025-11-15
CITATION
DOI: 10.26438/ijcse/v6i9.840843
Published: 2025-11-15

How to Cite

[1]
N. Sawhney, B. Kaur, and H. Kaur, “Probabilistic Support Vector Regression Classification Model for Credit Card Fraud Detection”, Int. J. Comp. Sci. Eng., vol. 6, no. 9, pp. 840–843, Nov. 2025.

Issue

Vol. 6 No. 9 (2018): IJCSE September Editor

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.