ORCID Profile
0000-0003-4261-8127
Current Organisation
University of Jordan
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Publisher: Elsevier BV
Date: 03-2019
Publisher: NADIA
Date: 30-06-2015
Publisher: Elsevier BV
Date: 12-2018
Publisher: Elsevier BV
Date: 06-2019
Publisher: Elsevier BV
Date: 09-2019
Publisher: Springer Science and Business Media LLC
Date: 05-03-2016
Publisher: Elsevier BV
Date: 09-2020
Publisher: Hindawi Limited
Date: 2015
DOI: 10.1155/2015/473283
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 c aigns 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.
Publisher: Elsevier BV
Date: 02-2020
Publisher: Elsevier BV
Date: 12-2020
No related grants have been discovered for Hossam Faris.