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

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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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Acknowledgments

This work is in part supported by the National Natural Science Foundation of China under Grant No. 61272529; Ministry of Education-China Mobile Research Fund under Grant No. MCM20130391; the Fundamental Research Funds for the Central Universities under Grant No. N130817003.

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Authors and Affiliations

  1. Software College, Northeastern University, Shenyang, 110819, China

    Ruiyun Yu, Xuanmiao An, Jia Shi & Oguti Ann Move

  2. School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian, 116024, China

    Bo Jin

  3. Computer Science and Engineering Department, The University of Texas at Arlington, Arlington, TX, 76019, USA

    Yonghe Liu

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  1. Ruiyun Yu
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  2. Xuanmiao An
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  3. Bo Jin
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Correspondence to Xuanmiao An.

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Yu, R., An, X., Jin, B. et al. Particle classification optimization-based BP network for telecommunication customer churn prediction. Neural Comput & Applic 29, 707–720 (2018). https://doi.org/10.1007/s00521-016-2477-3

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