C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
A statistical perspective on data mining
Future Generation Computer Systems - Special double issue on data mining
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Segmentation-based modeling for advanced targeted marketing
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Business applications of data mining
Communications of the ACM - Evolving data mining into solutions for insights
IEEE Intelligent Systems
Tree Induction for Probability-Based Ranking
Machine Learning
Design and application of hybrid intelligent systems
Data-intensive analytics for predictive modeling
IBM Journal of Research and Development
Mathematical sciences in the nineties
IBM Journal of Research and Development
A grid-based approach for enterprise-scale data mining
Future Generation Computer Systems - Special section: Data mining in grid computing environments
A grid-based approach for enterprise-scale data mining
Future Generation Computer Systems - Special section: Data mining in grid computing environments
A probabilistic estimation framework for predictive modeling analytics
IBM Systems Journal
A high-order feature synthesis and selection algorithm applied to insurance risk modelling
International Journal of Business Intelligence and Data Mining
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IBM's Underwriting Profitability Analysis application mines property and casualty (P&C) insurance policy and claims data to construct predictive models for insurance risks. UPA uses the ProbE (probabilistic estimation) predictive-modeling data-mining kernel to discover risk-characterization rules by analyzing large and noisy data sets. Each rule defines a distinct risk group and its risk level. To satisfy regulatory constraints, the risk groups are mutually exclusive and exhaustive. ProbE generates rules that are statistically rigorous, interpretable, and actuarially credible. The authors validated this approach in a joint development project with a P&C firm. The results suggest that this methodology provides significant value to P&C insurance risk management.