Indexed by:期刊论文

Date of Publication:2018-02-01

Journal:NEURAL COMPUTING & APPLICATIONS

Included Journals:SCIE、Scopus

Volume:29

Issue:3

Page Number:707-720

ISSN No.:0941-0643

Key Words:Particle classification optimization; BP neural network; PSO; Customer churn; Telecommunication networks

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.