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Customer Churn Prediction in Telecommunication Industry: With and without Counter-Example

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Nature-Inspired Computation and Machine Learning (MICAI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8857))

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Abstract

This study contributes to formalize customer churn prediction where rough set theory is used as one-class classifier and multi-class classifier to investigate the trade-off in the selection of an effective classification model for customer churn prediction. Experiments were performed to explore the performance of four different rule generation algorithms (i.e. exhaustive, genetic, covering and LEM2). It is observed that rough set as one-class classifier and multi-class classifier based on genetic algorithm yields more suitable performance as compared to the other three rule generation algorithms. Furthermore, by applying the proposed techniques (i.e. Rough sets as one-class and multi-class classifiers) on publicly available dataset, the results show that rough set as a multi - class classifier provides more accurate results for binary/multi-class classification problems.

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Authors and Affiliations

  1. Institute of Management Sciences, Hayatabad Phase VII Peshawar, Pakistan, Zip Code: 25000

    Adnan Amin, Changez Khan, Imtiaz Ali & Sajid Anwar

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  1. Adnan Amin
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  2. Changez Khan
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  3. Imtiaz Ali
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  4. Sajid Anwar
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Editor information

Editors and Affiliations

  1. Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan Dios Bátiz s/n, Col. Nueva Industrial Vallejo, 07738, Mexico City, Mexico

    Alexander Gelbukh

  2. Universidad Autónoma del Estado de Hidalgo, Área Académica de Computación y Electrónica, Carretera Pachuca-Tulancingo, , Km. 4.5, Col. Carboneras, Mineral de la Reforma,, 42180, Hidalgo, Mexico

    Félix Castro Espinoza

  3. Facultad de ciencias, Universidad Autónoma Nacional de México, Ciudad Universitaria, México DF, Mexico

    Sofía N. Galicia-Haro

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Amin, A., Khan, C., Ali, I., Anwar, S. (2014). Customer Churn Prediction in Telecommunication Industry: With and without Counter-Example. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds) Nature-Inspired Computation and Machine Learning. MICAI 2014. Lecture Notes in Computer Science(), vol 8857. Springer, Cham. https://doi.org/10.1007/978-3-319-13650-9_19

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  • DOI: https://doi.org/10.1007/978-3-319-13650-9_19

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13649-3

  • Online ISBN: 978-3-319-13650-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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