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Mobile Phone Customer Type Discrimination via Stochastic Gradient Boosting

Mobile Phone Customer Type Discrimination via Stochastic Gradient Boosting

Dan Steinberg, Mikhaylo Golovnya, Nicholas Scott Cardell
Copyright: © 2007 |Volume: 3 |Issue: 2 |Pages: 22
ISSN: 1548-3924|EISSN: 1548-3932|ISSN: 1548-3924|EISBN13: 9781615202089|EISSN: 1548-3924|DOI: 10.4018/jdwm.2007040104
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MLA

Steinberg, Dan, et al. "Mobile Phone Customer Type Discrimination via Stochastic Gradient Boosting." IJDWM vol.3, no.2 2007: pp.32-53. http://doi.org/10.4018/jdwm.2007040104

APA

Steinberg, D., Golovnya, M., & Cardell, N. S. (2007). Mobile Phone Customer Type Discrimination via Stochastic Gradient Boosting. International Journal of Data Warehousing and Mining (IJDWM), 3(2), 32-53. http://doi.org/10.4018/jdwm.2007040104

Chicago

Steinberg, Dan, Mikhaylo Golovnya, and Nicholas Scott Cardell. "Mobile Phone Customer Type Discrimination via Stochastic Gradient Boosting," International Journal of Data Warehousing and Mining (IJDWM) 3, no.2: 32-53. http://doi.org/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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