title: Negative correlation learning for customer churn prediction: a comparison study.
abstract: Recently, telecommunication companies have been paying more attention toward the problem of identification of customer churn behavior. In business, it is well known for service providers that attracting new customers is much more expensive than retaining existing ones. Therefore, adopting accurate models that are able to predict customer churn can effectively help in customer retention campaigns and maximizing the profit. In this paper we will utilize an ensemble of Multilayer perceptrons (MLP) whose training is obtained using negative correlation learning (NCL) for predicting customer churn in a telecommunication company. Experiments results confirm that NCL based MLP ensemble can achieve better generalization performance (high churn rate) compared with ensemble of MLP without NCL (flat ensemble) and other common data mining techniques used for churn analysis.
authors: King Abdulla II School for Information Technology, The University of Jordan, Amman 11942, Jordan.
authors: Ali
authors: 0000-0002-9282-5717
authors: A
authors: Rodan
authors: College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia.
authors: Ayham
authors: 0000-0001-5240-7302
authors: A
authors: Fayyoumi
authors: King Abdulla II School for Information Technology, The University of Jordan, Amman 11942, Jordan.
authors: Hossam
authors: H
authors: Faris
authors: King Abdulla II School for Information Technology, The University of Jordan, Amman 11942, Jordan.
authors: Jamal
authors: 0000-0001-6968-5616
authors: J
authors: Alsakran
authors: King Abdulla II School for Information Technology, The University of Jordan, Amman 11942, Jordan.
authors: Omar
authors: 0000-0002-6346-0106
authors: O
authors: Al-Kadi