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Activity Number: 435
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2009 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #304657
Title: Profit Analysis in Binary Prediction
Author(s): Michele Meisner*+
Companies: The College of New Jersey
Address: 2000 Pennington Road, Ewing, NJ, 08628-4700,
Keywords: binary classification ; profit analysis ; storm prediction ; bootstrapping
Abstract:

This study investigates a set of storm data that is available in an online journal called CSBIGS (Case Studies in Business, Industry and Government Statistics, http://www.bentley.edu/csbigs/csbigs-v1-n2.cfm). The data is comprised of a training sample of 11,803 observations and a test sample of 20,998 observations. The data is highly unbalanced and requires down-sampling or over-sampling techniques before the building of predictive models. Our study analyzes the data with various cost implications that may be useful for a variety of industries, including insurance companies, airline flight management, and inventory management for large-scale retailers. The data mining tools in this study include decision tree, boosted tree, logistic regression, and neural network.

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