Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
C4.5: programs for machine learning
C4.5: programs for machine learning
An introduction to computational learning theory
An introduction to computational learning theory
Machine Learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning - Special issue on learning with probabilistic representations
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Robust Classification for Imprecise Environments
Machine Learning
Visualizing the simple Baysian classifier
Information visualization in data mining and knowledge discovery
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Bayesian Networks for Knowledge-Based Authentication
IEEE Transactions on Knowledge and Data Engineering
A scoring model to detect abusive billing patterns in health insurance claims
Expert Systems with Applications: An International Journal
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Abstract--In this paper, we apply the weight of evidence reformulation of AdaBoosted naive Bayes scoring due to Ridgeway et al. [38] to the problem of diagnosing insurance claim fraud. The method effectively combines the advantages of boosting and the explanatory power of the weight of evidence scoring framework. We present the results of an experimental evaluation with an emphasis on discriminatory power, ranking ability, and calibration of probability estimates. The data to which we apply the method consists of closed personal injury protection (PIP) automobile insurance claims from accidents that occurred in Massachusetts during 1993 and were previously investigated for suspicion of fraud by domain experts. The data mimic the most commonly occurring data configuration--that is, claim records consisting of information pertaining to several binary fraud indicators. The findings of the study reveal the method to be a valuable contribution to the design of intelligible, accountable, and efficient fraud detection support.