Negative Correlation Learning for Customer Churn Prediction: A Comparison Study
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 m...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2015/473283 |
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| Summary: | 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. |
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| ISSN: | 2356-6140 1537-744X |