The application of the locally linear model tree on customer churn prediction

Amineh Ghorbani, Fattaneh Taghiyareh, Caro Lucas

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

11 Citations (Scopus)

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 languageEnglish
Title of host publicationSoCPaR 2009 - Soft Computing and Pattern Recognition
PublisherIEEE
Pages472-477
Number of pages6
ISBN (Print)9780769538792
DOIs
Publication statusPublished - 1 Dec 2009
EventInternational Conference on Soft Computing and Pattern Recognition, SoCPaR 2009 - Malacca, Malaysia
Duration: 4 Dec 20097 Dec 2009

Conference

ConferenceInternational Conference on Soft Computing and Pattern Recognition, SoCPaR 2009
CountryMalaysia
CityMalacca
Period4/12/097/12/09

Keywords

  • Customer churn
  • Locally linear model tree
  • Prediction

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