Random rough subspace based neural network ensemble for insurance fraud detection

Proceedings - 4th International Joint Conference on Computational Sciences and Optimization, CSO 2011

Volumn , Issue , 2011, Pages 1276-1280

  Xu, Wei ^a   Wang, Shengnan ^b   Zhang, Dailing ^b   Yang, Bo ^b  

^a Beijing Key Laboratory of Big Data Management and Analysis Methods   (China)

^b RENMIN UNIVERSITY OF CHINA   (China)

Author keywords

Ensemble; Insurance fraud detection; Neural network; Random rough subspace; Rough set

Indexed keywords

AUTOMOBILE INSURANCE; DATA INFORMATIONS; ENSEMBLE; ENSEMBLE STRATEGIES; EVALUATION CRITERIA; FRAUD DETECTION; INSURANCE CLAIMS; NEURAL NETWORK CLASSIFIER; NEURAL NETWORK ENSEMBLES; RANDOM ROUGH SUBSPACE; RECEIVE OPERATING CHARACTERISTICS; ROUGH SET; ROUGH SET REDUCTION; SUBSPACE BASED; TRAINED NEURAL NETWORKS;

AUTOMOBILES; COMPUTER CRIME; CRIME; INSURANCE; OPTIMIZATION; ROUGH SET THEORY;

NEURAL NETWORKS;

EID: 80052009149     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CSO.2011.213     Document Type: Conference Paper

References (19)

1. S. Viaene, R.A. Derrig, and G. Dedene, "A case study of applying boosting naive Bayes to claim fraud diagnosis, " IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 5, pp. 612-620, 2004.
2. S. Viaene, M. Ayuso, M. Guillen, D.V. Gheel, and G. Dedene, "Strategies for detecting fraudulent claims in the automobile insurance industry, " European Journal of Operational Research, vol. 176, pp. 565-583, 2007. (Pubitemid 44472574)
3. H.I. Weisberg, and R.A. Derrig, "Quantitative methods for detecting fraudulent automobile bodily injury claims, " Risques, vol. 35, pp. 75- 99, 1998.
4. E.B. Belhadji, G. Dionne, and F. Tarkhani, "A model for the detection of insurance fraud, " Geneva Papers on Risk and Insurance- Issues and Practice, vol. 25, pp. 517-539, 2000.
5. M. Artis, M. Ayuso, and M. Guillen, "Modelling different types of automobile insurance fraud behaviour in the Spanish market, " Insurance: Mathematics and Economics, vol. 24, pp. 67-81, 1999.
6. M. Artis, A. Mercedes, and M. Guillen, "Detection of automobile insurance fraud with discrete choice models and misclassified claims, " The Journal of Risk and Insurance, vol. 69, no. 3, pp. 325- 340, 2002. (Pubitemid 40830723)
7. S. Viaene, G. Dedene, and R.A. Derrig, "Auto claim fraud detection using Bayesian Learning neural networks, " Expert Systems with Applications, vol. 29, pp. 653-666, 2005. (Pubitemid 41230787)
8. H. He, J. Wang, W. Graco, and S. Hawkins, "Application of neural networks to detection of medical fraud, " Expert Systems with Applications, vol. 13, no. 4, pp. 329-336, 1997. (Pubitemid 127401099)
9. P.L. Brockett, X. Xia, and R.A. Derrig, "Using Kohonen's selforganizing feature map to uncover automobile bodily injury claims fraud, " The Journal of Risk and Insurance, vol. 65, no. 2, pp. 245-274, 1998. (Pubitemid 128461598)
10. W.S. Yang, and S.Y. Hwang, "A process-mining framework for the detection of healthcare fraud and abuse, " Expert Systems with Applications, vol. 31, pp. 56-68, 2006.
11. T.M. Padmaja, N. Dhulipalla, R.S. Bapi, and P.R. Krishna, "Unbalanced data classification using extreme outlier elimination and sampling techniques for fraud detection, " In Proceeding of the 15th International Conference on Advanced Computing and Communications, IEEE Press, 2007, pp. 511-516.
12. L. Bermudez, J.M. Perez, M. Ayuso, E. Gomez, and F.J. Vazquez, "A Bayesian dichotomous model with asymmetric link for fraud in insurance, " Insurance: Mathematics and Economics, vol. 42, pp. 779- 786, 2008.
13. K. Yamanishi, J.I. Takeuchi, G. Williams, and P. Nilne, "On-line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms, " Data Mining and Knowledge Discovery, vol. 8, pp. 275-300, 2004. (Pubitemid 39019964)
14. C. Phua, D. Alahakoon, and V. Lee, "Minority report in fraud detection: classification of skewed data, " SIGKDD Explorations, vol. 6, no. 1, pp. 50-59, 2004.
15. J. Czyz, L. Kittler, and L. Vandendorpe, "Multiple classifier combination for face-based identity verification, " Pattern Recognition, vol. 37, pp. 1459-1469, 2004. (Pubitemid 38653374)
16. L. Breiman, "Stacked Regressions, " Machine Learning, vol. 24, pp. 49-64, 1996. (Pubitemid 126724379)
17. J. Mao, "A case study on bagging, boosting and basic ensembles of neural networks for OCR, " In Proceeding of IEEE International Joint Conference on Neural Networks, IEEE Press, 1998, pp. 1828-1833.
18. T.K. Ho, "The random subspace method for constructing decision forests, " IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 20, pp. 832-844, 1998. (Pubitemid 128741345)
19. Q. Hu, D. Yu, Z. Xie, and X. Li, "EROS: Ensemble rough subspaces, " Pattern Recognition, vol. 40, pp. 3728-3739, 2007. (Pubitemid 47223293)