Cost-sensitive learning and decision making for massachusetts pip claim fraud data
Stijn Viaene, Richard A. Derrig, Guido Dedene · International Journal of Intelligent Systems · 2004
In this article, we investigate the issue of cost-sensitive classification for a data set of Massachusetts closed personal injury protection (PIP) automobile insurance claims that were previously investigated for suspicion of fraud by domain experts and for which we obtained cost information. After a theoretical exposition on cost-sensitive learning and decision-making methods, we then apply these methods to the claims data at hand to contrast the predictive performance of the documented methods for a selection of decision tree and rule learners. We use standard logistic regression and (smoothed) naive Bayes as benchmarks. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 1197–1215, 2004.