Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2015, Article ID 473283, 7 pages
Research Article

Negative Correlation Learning for Customer Churn Prediction: A Comparison Study

1King Abdulla II School for Information Technology, The University of Jordan, Amman 11942, Jordan
2College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia

Received 17 June 2014; Revised 23 August 2014; Accepted 7 September 2014

Academic Editor: Shifei Ding

Copyright © 2015 Ali Rodan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [8 citations]

The following is the list of published articles that have cited the current article.

  • Ali Rodan, and Hossam Faris, “Echo State Network with SVM-readout for customer churn prediction,” 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), pp. 1–5, . View at Publisher · View at Google Scholar
  • Mehreen Ahmed, Imran Siddiqi, Hammad Afzal, and Behram Khan, “MCS: Multiple classifier system to predict the churners in the telecom industry,” 2017 Intelligent Systems Conference (IntelliSys), pp. 678–683, . View at Publisher · View at Google Scholar
  • Yizhe Ge, Shan He, Jingyue Xiong, and Donald E. Brown, “Customer churn analysis for a software-as-a-service company,” 2017 Systems and Information Engineering Design Symposium (SIEDS), pp. 106–111, . View at Publisher · View at Google Scholar
  • Amjad Hudaib, Reham Dannoun, Osama Harfoushi, Ruba Obiedat, and Hossam Faris, “Hybrid Data Mining Models for Predicting Customer Churn,” International Journal of Communications, Network and System Sciences, vol. 08, no. 05, pp. 91–96, 2015. View at Publisher · View at Google Scholar
  • Alae Chouiekh, and El Hassane Ibn El Haj, “Machine learning techniques applied to prepaid subscribers: Case study on the telecom industry of Morocco,” 2017 Intelligent Systems and Computer Vision, ISCV 2017, 2017. View at Publisher · View at Google Scholar
  • Mehreen Ahmed, Hammad Afzal, Awais Majeed, and Behram Khan, “A Survey of Evolution in Predictive Models and Impacting Factors in Customer Churn,” Advances in Data Science and Adaptive Analysis, vol. 09, no. 03, pp. 1750007, 2017. View at Publisher · View at Google Scholar
  • Adnan Amin, Feras Al-Obeidat, Babar Shah, May Al Tae, Changez Khan, Hamood Ur Rehman Durrani, and Sajid Anwar, “Just-in-time customer churn prediction in the telecommunication sector,” The Journal of Supercomputing, 2017. View at Publisher · View at Google Scholar
  • Mahreen Ahmed, Hammad Afzal, Imran Siddiqi, Muhammad Faisal Amjad, and Khawar Khurshid, “Exploring nested ensemble learners using overproduction and choose approach for churn prediction in telecom industry,” Neural Computing and Applications, 2018. View at Publisher · View at Google Scholar