Applying migrating birds optimization to credit card fraud detection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7867 LNAI, Issue , 2013, Pages 416-427

  Duman, Ekrem ^a   Elikucuk, Ilker ^b  

^a OZYEGIN UNIVERSITY   (Turkey)

^b Intertech   (Turkey)

Author keywords

Credit cards; Fraud; Genetic algorithms; Migrating birds optimization algorithm

Indexed keywords

COMBINATORIAL OPTIMIZATION PROBLEMS; CREDIT CARD FRAUD DETECTIONS; CREDIT CARDS; FLIGHT FORMATION; FRAUD; META HEURISTIC ALGORITHM; OPTIMIZATION ALGORITHMS; QUADRATIC ASSIGNMENT PROBLEMS;

BIRDS; COMBINATORIAL OPTIMIZATION; CRIME; DATA MINING; OPTIMIZATION;

GENETIC ALGORITHMS;

EID: 84892838750     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40319-4_36     Document Type: Conference Paper

References (28)

1. Duman, E., Uysal, M., Alkaya, A.: Migrating Birds Optimization: A new metaheuristic approach and its performance on quadratic assignment problem. Information Sciences 217, 65-77 (2012)
2. Bolton, R.J., Hand, D.J.: Statistical fraud detection: A review. Statistical Science 28(3), 235-255 (2002)
3. Kou, Y., et al.: Survey of fraud detection techniques. In: Proceedings of the 2004 IEEE International Conference on Networking, Sensing and Control, Taipei, Taiwan, March 21-23 (2004)
4. Phua, C., et al.: A comprehensive survey of data mining-based fraud detection research. Artificial Intelligence Review (2005)
5. Sahin, Y., Duman, E.: An overview of business domains where fraud can take place, and a survey of various fraud detection techniques
6. Brause, R., Langsdorf, T., Hepp, M.: Neural data mining for credit card fraud detection. In: Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence (1999)
7. Hanagandi, V., Dhar, A., Buescher, K.: Density-Based Clustering and Radial Basis Function Modeling to Generate Credit Card Fraud Scores. In: Proceedings of the IEEE/IAFE 1996 Conference (1996)
8. Juszczak, P., Adams, N.M., Hand, D.J., Whitrow, C., Weston, D.J.: Off-the-peg and bespoke classifiers for fraud detection. Computational Statistics & Data Analysis 52(9) (2008)
9. Quah, J.T., Sriganesh, M.: Real-time credit card fraud detection using computational intelligence. Expert Systems with Applications 35(4) (2008)
10. Shen, A., Tong, R., Deng, Y.: Application of classification models on credit card fraud detection. In: International Conference on Service Systems and Service Management, Chengdu, China (June 2007)
11. Wheeler, R., Aitken, S.: Multiple algorithms for fraud detection. Knowledge-Based Systems 13(2/3) (2000)
12. Chen, R.-C., Chiu, M.-L., Huang, Y.-L., Chen, L.-T.: Detecting credit card fraud by using questionnaire-responded transaction model based on SVMs. In: Yang, Z.R., Yin, H., Everson, R.M. (eds.) IDEAL 2004. LNCS, vol. 3177, pp. 800-806. Springer, Heidelberg (2004)
13. Han, J., Camber, M.: Data mining concepts and techniques. Morgan Kaufman, San Diego (2000)
14. Quah, J.T.S., Srinagesh, M.: Real-time credit fraud detection using computational intelligence. Expert Systems with Applications 35, 1721-1732 (2008)
15. Panigrahi, S., Kundu, A., Sural, S., Majumdar, A.: Credit Card Fraud Detection: A Fusion Approach Using Dempster-Shafer Theory and Bayesian Learning. Information Fusion, 354-363 (2009)
16. Sanchez, D., Vila, M.A., Cerda, L., Serrano, J.M.: Association rules applied to credit card fraud detection. Expert Systems with Applications 36, 3630-3640 (2009)
17. Kim, M., Han, I.: The Discovery of Experts' Decision Rules from Qualitative Bankrupcy Data Using Genetic Algorithms. Expert Systems with Applications 25, 637-646 (2003)
18. Gadi, M.F.A., Wang, X., do Lago, A.P.: Credit Card Fraud Detection with Artificial Immune System. In: Bentley, P.J., Lee, D., Jung, S. (eds.) ICARIS 2008. LNCS, vol. 5132, pp. 119-131. Springer, Heidelberg (2008)
19. Duman, E., Ozcelik, M.H.: Detecting credit card fraud by genetic algorithm and scatter search. Expert Systems with Applications 38, 13057-13063 (2011)
20. Duman, E., Sahin, Y.: A Comparison of Classification Models on Credit Card Fraud Detection with respect to Cost-Based Performance Metrics. In: Duman, E., Atiya, A. (eds.) Use of Risk Analysis in Computer-Aided Persuasion. NATO Science for Peace and Security Series E: Human and Societal Dynamics, vol. 88, pp. 88-99. IOS Press (2011)
21. Gartner Reports, May 10, 2010 from the World Wide Web, http://www.gartner.com
22. Mena, J.: Investigate Data Mining for Security and Criminal Detection. Butterworth-Heinemann, Amsterdam (2003)
23. Ling, C.X., Sheng, V.S., Yang, Q.: Test Strategies for Cost-Sensitive Decision Trees. IEEE Transactions on Knowledge and Data Engineering 18(8) (2006)
24. Liu, X.: A Benefit-Cost Based Method for Cost-Sensitive Decision Trees. In: 2009 WRI Global Congress on Intelligent Systems, pp. 463-467 (2009)
25. Cutts, C.J., Speakman, J.R.: Energy savings in formation flight of pink-footed geese. J. Exp. Biol. 189, 251-261 (1994)
26. Lissaman, P.B.S., Shollenberger, C.A.: Formation flight of birds. Science 168, 1003-1005 (1970)
27. Hummel, D., Beukenberg, M.: Aerodynamsiche Interferenseffekte beim formationsflug von vögeln. J. Orn. 130, 15-24 (1989)
28. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) Algorithm. Journal of Global Optimization 39(3), 459-471 (2007)