Abstract
Acquiring new customers in any business is much more expensive than trying to keep the existing ones. Thus many prediction models are presented to detect churning customers. The objective of this paper was to improve the predictive accuracy and interpretability of churn detection. For this purpose, the application of the locally linear model tree (LOLIMOT) algorithm, which integrates the advantage of neural networks, tree model and fuzzy modeling, was experimented. Applied to the data of a major telecommunication company, the method is found to improve prediction accuracy significantly compared to other algorithms, such as artificial neural networks, decision trees, and logistic regression. The results also indicate that LOLIMOT can have accurate outcome in extremely unbalanced datasets.
Original language | English |
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Title of host publication | SoCPaR 2009 - Soft Computing and Pattern Recognition |
Publisher | IEEE |
Pages | 472-477 |
Number of pages | 6 |
ISBN (Print) | 9780769538792 |
DOIs | |
Publication status | Published - 1 Dec 2009 |
Event | International Conference on Soft Computing and Pattern Recognition, SoCPaR 2009 - Malacca, Malaysia Duration: 4 Dec 2009 → 7 Dec 2009 |
Conference
Conference | International Conference on Soft Computing and Pattern Recognition, SoCPaR 2009 |
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Country/Territory | Malaysia |
City | Malacca |
Period | 4/12/09 → 7/12/09 |
Keywords
- Customer churn
- Locally linear model tree
- Prediction