Boosting Naive Bayes for Claim Fraud Diagnosis

In this paper we apply the weight of evidence reformulation of AdaBoosted naive Bayes scoring due to Ridgeway et al. (1998) for the diagnosis of insurance claim fraud. The method effectively combines the advantages of boosting and the modelling power and representational attractiveness of the probabilistic weight of evidence scoring framework. We present the results of an experimental comparison with an emphasis on both discriminatory power and calibration of probability estimates. The data on which we evaluate the method consists of a representative set of closed personal injury protection automobile insurance claims from accidents that occurred in Massachusetts during 1993. The findings of the study reveal the method to be a valuable contribution to the design of effective, intelligible, accountable and efficient fraud detection support.

[1]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[2]  D. Titterington,et al.  Comparison of Discrimination Techniques Applied to a Complex Data Set of Head Injured Patients , 1981 .

[3]  David J. Hand,et al.  Construction and Assessment of Classification Rules , 1997 .

[4]  Thomas Richardson,et al.  Interpretable Boosted Naïve Bayes Classification , 1998, KDD.

[5]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.

[6]  Ron Kohavi,et al.  The Case against Accuracy Estimation for Comparing Induction Algorithms , 1998, ICML.

[7]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

[8]  Paul N. Bennett.  Assessing the Calibration of Naive Bayes Posterior Estimates , 2000 .

[9]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[10]  Thomas Richardson,et al.  Boosting methodology for regression problems , 1999, AISTATS.

[11]  D J Spiegelhalter,et al.  Probabilistic prediction in patient management and clinical trials. , 1986, Statistics in medicine.

[12]  Ron Kohavi,et al.  Improving simple Bayes , 1997 .

[13]  D. J. Spiegelhalter,et al.  Statistical and Knowledge‐Based Approaches to Clinical Decision‐Support Systems, with an Application in Gastroenterology , 1984 .

[14]  Guido Dedene,et al.  A Comparison of State-of-The-Art Classification Techniques for Expert Automobile Insurance Claim Fraud Detection , 2002 .

[15]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[16]  J. Copas.  Plotting p against x , 1983 .

[17]  Eric Bauer,et al.  An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.

[18]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[19]  Tom Fawcett,et al.  Robust Classification for Imprecise Environments , 2000, Machine Learning.

[20]  Bianca Zadrozny,et al.  Learning and making decisions when costs and probabilities are both unknown , 2001, KDD '01.