Cost-sensitive learning and decision making for massachusetts PIP claim fraud data

International Journal of Intelligent Systems

Volumn 19, Issue 12, 2004, Pages 1197-1215

  Viaene, Stijn ^a,b   Derrig, Richard A ^c   Dedene, Guido ^a,b,d  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b VLERICK BUSINESS SCHOOL   (Belgium)

^c Automobile IB Masssachusetts   (United States)

^d UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

BUSINESS FUNCTIONS; COST-SENSITIVE LEARNING; DATA CONSOLIDATION; DECISION TREE; PERSONAL INJURY PROTECTION (PIP);

COMPUTER CRIME; DECISION THEORY; INSURANCE; INVESTMENTS; REGRESSION ANALYSIS; THEOREM PROVING; TREES (MATHEMATICS);

LEARNING SYSTEMS;

EID: 10044276875     PISSN: 08848173     EISSN: None     Source Type: Journal    
DOI: 10.1002/int.20049     Document Type: Article

References (43)

1. Viaene S, Dedene G. Insurance fraud: Issues and challenges. Geneva Pap Risk Ins Issues Practice 2004;29:313-333.
2. Derrig RA. Insurance fraud. J Risk Ins 2002;69:271-287.
3. Provost F, Fawcett T. Robust classification for imprecise environments. Mach Learn 2001;42:203-231.
4. Provost F, Fawcett T, Kohavi R. The case against accuracy estimation for comparing classifiers. In: 15th Int Conf on Machine Learning, Madison, WI, July 1998.
5. Baesens B, Viaene S, Van den Poel D, Vanthienen J, Dedene G. Bayesian neural network learning for repeat purchase modelling in direct marketing. Eur J Oper Res 2002;138:191-211.
6. Bradley AP. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn 1997;30:1145-1159.
7. Hand DJ, Till RJ. A simple generalisation of the area under the ROC curve for multiple class classification problems. Mach Learn 2001;45:171-186.
8. Hand DJ. Construction and assessment of classification rules. Chichester, England: Wiley; 1997.
9. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982;143:29-36.
10. Margineantu DD. Methods for cost-sensitive learning. PhD thesis. Corvallis, OR: Oregon State University; 2001.
11. Bradford JP, Kunz C, Kohavi R, Brunk C, Brodley CE. Pruning decision trees with misclassification costs. In: 10th Eur Conf on Machine Learning, Chemnitz, Germany, April 1998.
12. Gama J. A cost-sensitive iterative Bayes. In: 17th Int Conf on Machine Learning, Workshop on Cost-Sensitive Learning, Stanford, CA, June-July 2000.
13. Witten IH, Frank E. Data mining: Practical machine learning tools and techniques with Java implementations. San Mateo, CA: Morgan Kaufmann; 1999.
14. Quinlan JR. C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann; 1993.
15. Viaene S, Derrig RA, Baesens B, Dedene G. A comparison of state-of-the-art classification techniques for expert automobile insurance claim fraud detection. J Risk Ins 2002;69:373-421.
16. Duda RO, Hart PE, Stork DG. Pattern classification. New York: Wiley; 2000.
17. Zadrozny B, Elkan C. Learning and making decisions when costs and probabilities are both unknown. In: 7th ACM SIGKDD Conf on Knowledge Discovery in Data Mining, San Francisco, CA, August 2001.
18. Elkan C. The foundations of cost-sensitive learning. In: 27th Int Joint Conf on Artificial Intelligence, Seattle, WA, August 2001.
19. Zadrozny B, Elkan C. Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers. In: 18th Int Conf on Machine Learning, Williams College, MA, June-July 2001.
20. Cestnik B. Estimating probabilities: A crucial task in machine learning. In: 9th Eur Conf on Artificial Intelligence, Stockholm, Sweden, August 1990.
21. Cussens J. Bayes and pseudo-Bayes estimates of conditional probabilities and their reliability. In: 6th Eur Conf on Machine Learning, Vienna, Austria, April 1993.
22. Domingos P. MetaCost: A general method for making classifiers cost-sensitive. In: 5th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, San Diego, CA, August 1999.
23. Breiman L. Bagging predictors. Mach Learn 1996;24:123-140.
24. Ting KM, Zheng Z. Improving the performance of boosting for naive Bayesian classification. In: 3rd Pacific-Asia Conf on Knowledge Discovery and Data Mining, Beijing, China, April 1999.
25. Domingos P. Knowledge discovery via multiple models. Intell Data Anal 1998;2:187-202.
26. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees (CART). Belmont, CA: Wadsworth International Group; 1984.
27. Bauer E, Kohavi R. An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Mach Learn 1999;36:105-139.
28. Opitz D, Maclin R. Popular ensemble methods: An empirical study. J Artif Intell Res 1999;11:169-198.
29. Shapire RE, Freund Y, Bartlett P, Lee WS. Boosting the margin: A new explanation for the effectiveness of voting methods. Ann Stat 1998;26:1651-1686.
30. Webb GI. MultiBoosting: A technique for combining boosting and wagging. Mach Learn 2000;40:159-196.
31. Shapire RE, Singer Y. Improved boosting algorithms using confidence-rated predictions. Mach Learn 1999;37:297-336.
32. Freund Y, Shapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 1997;55:119-139.
33. Friedman JH, Hastie T, Tibshirani R. Additive logistic regression: A statistical view of boosting. Ann Stat 2000;38:337-374.
34. Schapire RE. The boosting approach to machine learning: An overview. In: MSRI Workshop on Nonlinear Estimation and Classification, Berkeley, CA, March 2002.
35. Fan W, Stolfo SJ, Zhang J, Chan PK. AdaCost: Misclassification cost-sensitive boosting. In: 16th Int Conf on Machine Learning, Bled, Slovenia, June 1999.
36. Derrig RA, Weisberg HI. AIB PIP screening experiment final report - Understanding and improving the claim investigation process. AIB Cost Containment/Fraud Filing DOI Docket R98-41 (IFRR-267), Automobile Insurers Bureau of Massachusetts, Boston, MA, 1998.
37. Weisberg HI, Derrig RA. Fraud and automobile insurance: A report on the baseline study of bodily injury claims in Massachusetts. J Ins Regul 1991;9:497-541.
38. Weisberg HI, Derrig RA. Identification and investigation of suspicious claims. AIB Cost Containment/Fraud Filing DOI Docket R95-12 (IFRR-170), Automobile Insurers Bureau of Massachusetts, Boston, MA, 1995.
39. Weisberg HI, Derrig RA. Quantitative methods for detecting fraudulent automobile bodily injury claims. Risques 1998;35:75-101.
40. Iba W, Langley P. Induction of one-level decision trees. In: 9th Int Workshop on Machine Learning, Aberdeen, UK, July 1992.
41. Holte RC. Very simple classification rules perform well on most commonly used datasets. Mach Learn 1993;11:63-90.
42. Frank E, Witten IH. Generating accurate rule sets without global optimization. In: 15th Int Conf on Machine Learning, Madison, WI, July 1998.
43. Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers. Mach Learn 1997; 29:131-163.