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When will you have a new mobile phone? An empirical answer from big data

Ma, Qingli, Zhang, Sihai, Zhou, Wuyang, Yu, Shui and Wang, Chonggang 2016, When will you have a new mobile phone? An empirical answer from big data, IEEE access, vol. 4, pp. 10147-10157, doi: 10.1109/ACCESS.2016.2635805.

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Title When will you have a new mobile phone? An empirical answer from big data
Author(s) Ma, Qingli
Zhang, Sihai
Zhou, Wuyang
Yu, ShuiORCID iD for Yu, Shui orcid.org/0000-0003-4485-6743
Wang, Chonggang
Journal name IEEE access
Volume number 4
Start page 10147
End page 10157
Total pages 11
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2016-12-15
ISSN 2169-3536
Keyword(s) mobile big data
attribute selection
imbalance problem
phone changing prediction
machine learning
Summary When and why people change their mobile phones are important issues in mobile communications industry, because it will impact greatly on the marketing strategy and revenue estimation for both mobile operators and manufactures. It is a promising way to take use of big data to analyze and predict the phone changing event. In this paper, based on mobile user big data, first through statistical analysis, we find that three important probability distributions, i.e., power-law, log-normal, and geometric distribution, play an important role in the user behaviors. Second, the relationships between eight selected attributes and phone changing are built, for example, young people have greater intention to change their phones if they are using the phones belonging to the low occupancy phones or feature phones. Third, we verified the performance of four prediction models on phone changing event under three scenarios. Information gain ratio was used to implement attribute selection and then sampling method, cost-sensitive together with standard classifiers were used to solve imbalanced phone changing event. Experiment results show our proposed enhanced backpropagation neural network in the undersampling scenario can attain better prediction performance.
Language eng
DOI 10.1109/ACCESS.2016.2635805
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 0 Not Applicable
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2016, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30092575

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.