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Mobile Phone Customer Type Discrimination via Stochastic Gradient Boosting

Mobile Phone Customer Type Discrimination via Stochastic Gradient Boosting
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Author(s): Dan Steinberg (Salford Systems, USA), Mikhaylo Golovnya (Salford Systems, USA) and Nicholas Scott Cardell (Salford Systems, USA)
Copyright: 2009
Pages: 25
Source title: Mobile Computing: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): David Taniar (Monash University, Australia)
DOI: 10.4018/978-1-60566-054-7.ch217

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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 increasingly 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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