Mobile Phone Customer Type Discrimination via Stochastic Gradient Boosting

Mobile Phone Customer Type Discrimination via Stochastic Gradient Boosting

Dan Steinberg (Salford Systems, USA), Mikhaylo Golovnya (Salford Systems, USA) and Nicholas Scott Cardell (Salford Systems, USA)
Copyright: © 2007 |Pages: 22
DOI: 10.4018/jdwm.2007040104
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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.

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