Segmentation-based modeling for advanced targeted marketing
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Probabilistic Estimation-Based Data Mining for Discovering Insurance Risks
IEEE Intelligent Systems
Why Discretization Works for Naive Bayesian Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Passenger-based predictive modeling of airline no-show rates
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Data-intensive analytics for predictive modeling
IBM Journal of Research and Development
Mathematical sciences in the nineties
IBM Journal of Research and Development
Embedded predictive modeling in a parallel relational database
Proceedings of the 2006 ACM symposium on Applied computing
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
Cascade evaluation of clustering algorithms
ECML'06 Proceedings of the 17th European conference on Machine Learning
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IBM ProbE (for probabilistic estimation) is an extensible, embeddable, and scalable modeling engine, particularly well-suited for implementing segmentation-based modeling techniques, wherein data records are partitioned into segments and separate predictive models are developed for each segment. We describe the ProbE framework and discuss two key business solutions that have been built using ProbE: the IBM Underwriting Profitability Analysis for insurance risk management, and the IBM Advanced Targeted Marketing for Single Events for direct mail database marketing.