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Predicting Mobile Portability Across Telecommunication Networks Using the Integrated-KLR

Predicting Mobile Portability Across Telecommunication Networks Using the Integrated-KLR

Ayodeji Samuel Makinde, Abayomi O. Agbeyangi, Wilson Nwankwo
Copyright: © 2021 |Volume: 17 |Issue: 3 |Pages: 13
ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781799859642|DOI: 10.4018/IJIIT.2021070104
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MLA

Makinde, Ayodeji Samuel, et al. "Predicting Mobile Portability Across Telecommunication Networks Using the Integrated-KLR." IJIIT vol.17, no.3 2021: pp.1-13. http://doi.org/10.4018/IJIIT.2021070104

APA

Makinde, A. S., Agbeyangi, A. O., & Nwankwo, W. (2021). Predicting Mobile Portability Across Telecommunication Networks Using the Integrated-KLR. International Journal of Intelligent Information Technologies (IJIIT), 17(3), 1-13. http://doi.org/10.4018/IJIIT.2021070104

Chicago

Makinde, Ayodeji Samuel, Abayomi O. Agbeyangi, and Wilson Nwankwo. "Predicting Mobile Portability Across Telecommunication Networks Using the Integrated-KLR," International Journal of Intelligent Information Technologies (IJIIT) 17, no.3: 1-13. http://doi.org/10.4018/IJIIT.2021070104

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Abstract

Mobile number portability (MNP) across telecommunication networks entails the movement of a customer from one mobile service provider to another. This, often, is as a result of seeking better service delivery or personal choice. Churning prediction techniques seek to predict customers tending to churn and allow for improved customer sustenance campaigns and the cost therein through an improved service efficiency to customer. In this paper, MNP predicting model using integrated kernel logistic regression (integrated-KLR) is proposed. The Integrated-KLR is a combination of kernel logistic regression and expectation-maximization clustering which helps in proactively detecting potential customers before defection. The proposed approach was evaluated with five others, mostly used algorithms: SOM, MLP, Naïve Bayes, RF, J48. The proposed iKLR outperforms the other algorithms with ROC and PRC of 0.856 and 0.650, respectively.