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Cost‐sensitive learning and decision making for massachusetts pip claim fraud data
Author(s) -
Viaene Stijn,
Derrig Richard A.,
Dedene Guido
Publication year - 2004
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20049
Subject(s) - decision tree , computer science , exposition (narrative) , logistic regression , automobile insurance , bayes' theorem , machine learning , data set , naive bayes classifier , domain (mathematical analysis) , artificial intelligence , set (abstract data type) , data mining , actuarial science , support vector machine , mathematics , economics , bayesian probability , art , mathematical analysis , literature , programming language
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.