Mobile Phone Customer Type Discrimination via Stochastic Gradient Boosting

Dan Steinberg, Mikhaylo Golovnya and Nicholas Scott Cardell
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Dan Steinberg: Salford Systems, USA
Mikhaylo Golovnya: Salford Systems, USA
Nicholas Scott Cardell: Salford Systems, USA

International Journal of Data Warehousing and Mining (IJDWM), 2007, vol. 3, issue 2, 32-53

Abstract: Mobile phone customers face many choices regarding handset hardware, add-on services, and features to subscribe to from their service providers. Mobile phone companies are now increas-ingly interested in the drivers of migration to third generation (3G) hardware and services. Using real world data provided to the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2006 Data Mining Competition we explore the effectiveness of Friedman’s stochastic gradient boosting (Multiple Additive Regression Trees [MART]) for the rapid development of a high performance predictive model.

Date: 2007
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