Earticle

초록

영어

Churn prediction is an important task for Customer Relationship Management (CRM) in telecommunication companies. Accurate churn prediction helps CRM in planning effective strategies to retain their valuable customers. However, churn prediction is a complex and challenging task. In this paper, a hybrid churn prediction model is proposed based on combining two approaches; Neighborhood Cleaning Rules (NCL) and Particle Swarm Optimization (PSO). NCL is applied in the preprocessing stage for handling the imbalanced churn data; and eliminating outliers and unrepresentative data. In the next stage, a Constricted PSO is applied for developing the final prediction model. The developed model is evaluated and compared with a baseline PSO model. The proposed hybrid model is compared also with Artificial Neural Networks (ANN) and Decision trees (DT) models which are traditional and common approaches used in the literature for churn prediction. The experimental results show that the proposed hybrid model outperforms the baseline PSO model, ANN and DT in terms of accuracy and actual churn rate.

목차

Abstract
 1. Introduction
 2. The Proposed Hybrid Approach
 3. Neighborhood Cleaning Rules (NCL)
 4. Particle Swarm Optimization (PSO)
 5. Dataset Description
 6. Model Evaluation Criteria
 7. Experiments and Results
  7.1. Basic CPSO Model
  7.2. NCL+CPSO Model
  7.3. Comparison
 8. Conclusion
 References

키워드

저자

• Hossam Faris [ Business Information Technology Department, the University of Jordan, Amman, Jordan ]

참고문헌

발행기관

간행물

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• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605