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Neural Computing and Applications

Erschienen in:

19.07.2016 | Original Article

Particle classification optimization-based BP network for telecommunication customer churn prediction

verfasst von: Ruiyun Yu, Xuanmiao An, Bo Jin, Jia Shi, Oguti Ann Move, Yonghe Liu

Erschienen in: Neural Computing and Applications | Ausgabe 3/2018

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Abstract

Customer churn prediction is critical for telecommunication companies to retain users and provide customized services. In this paper, a particle classification optimization-based BP network for telecommunication customer churn prediction (PBCCP) algorithm is proposed, which iteratively executes the particle classification optimization (PCO) and the particle fitness calculation (PFC). PCO classifies the particles into three categories according to their fitness values, and updates the velocity of different category particles using distinct equations. PFC calculates the fitness value of a particle in each forward training process of a BP neural network. PBCCP optimizes the initial weights and thresholds of the BP neural network, and brings remarkable improvement on customer churn prediction accuracy.

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Titel: Particle classification optimization-based BP network for telecommunication customer churn prediction
verfasst von: Ruiyun Yu
Xuanmiao An
Bo Jin
Jia Shi
Oguti Ann Move
Yonghe Liu
Publikationsdatum: 19.07.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 3/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2477-3

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