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A reference model for customer-centric data mining with support vector machines

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  • Lessmann, Stefan
  • Voß, Stefan

Abstract

Supervised classification is an important part of corporate data mining to support decision making in customer-centric planning tasks. The paper proposes a hierarchical reference model for support vector machine based classification within this discipline. The approach balances the conflicting goals of transparent yet accurate models and compares favourably to alternative classifiers in a large-scale empirical evaluation in real-world customer relationship management applications. Recent advances in support vector machine oriented research are incorporated to approach feature, instance and model selection in a unified framework.

Suggested Citation

  • Lessmann, Stefan & Voß, Stefan, 2009. "A reference model for customer-centric data mining with support vector machines," European Journal of Operational Research, Elsevier, vol. 199(2), pages 520-530, December.
  • Handle: RePEc:eee:ejores:v:199:y:2009:i:2:p:520-530
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    Cited by:

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    2. Fan, Zhi-Ping & Sun, Minghe, 2015. "Behavior-aware user response modeling in social media: Learning from diverse heterogeneous dataAuthor-Name: Chen, Zhen-Yu," European Journal of Operational Research, Elsevier, vol. 241(2), pages 422-434.
    3. Blanquero, R. & Carrizosa, E. & Jiménez-Cordero, A. & Martín-Barragán, B., 2019. "Functional-bandwidth kernel for Support Vector Machine with Functional Data: An alternating optimization algorithm," European Journal of Operational Research, Elsevier, vol. 275(1), pages 195-207.
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    6. Martin-Barragan, Belen & Lillo, Rosa & Romo, Juan, 2014. "Interpretable support vector machines for functional data," European Journal of Operational Research, Elsevier, vol. 232(1), pages 146-155.
    7. Gattermann-Itschert, Theresa & Thonemann, Ulrich W., 2021. "How training on multiple time slices improves performance in churn prediction," European Journal of Operational Research, Elsevier, vol. 295(2), pages 664-674.
    8. Tang, Leilei & Thomas, Lyn & Fletcher, Mary & Pan, Jiazhu & Marshall, Andrew, 2014. "Assessing the impact of derived behavior information on customer attrition in the financial service industry," European Journal of Operational Research, Elsevier, vol. 236(2), pages 624-633.
    9. Edouard Ribes & Karim Touahri & Benoît Perthame, 2017. "Employee turnover prediction and retention policies design: a case study," Working Papers hal-01556746, HAL.
    10. Jian Shi & Benlian Xu, 2016. "Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function," JRFM, MDPI, vol. 9(4), pages 1-10, November.
    11. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W., 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," European Journal of Operational Research, Elsevier, vol. 269(2), pages 760-772.
    12. Gitae Kim & Bongsug Chae & David Olson, 2013. "A support vector machine (SVM) approach to imbalanced datasets of customer responses: comparison with other customer response models," Service Business, Springer;Pan-Pacific Business Association, vol. 7(1), pages 167-182, March.
    13. Chen, Zhen-Yu & Fan, Zhi-Ping & Sun, Minghe, 2012. "A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data," European Journal of Operational Research, Elsevier, vol. 223(2), pages 461-472.
    14. Chen, Zhen-Yu & Fan, Zhi-Ping & Sun, Minghe, 2019. "Individual-level social influence identification in social media: A learning-simulation coordinated method," European Journal of Operational Research, Elsevier, vol. 273(3), pages 1005-1015.
    15. Minnu F. Pynadath & T. M. Rofin & Sam Thomas, 2023. "Evolution of customer relationship management to data mining-based customer relationship management: a scientometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3241-3272, August.
    16. Oosterlinck, Dieter & Benoit, Dries F. & Baecke, Philippe, 2020. "From one-class to two-class classification by incorporating expert knowledge: Novelty detection in human behaviour," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1011-1024.
    17. Brandner, Hubertus & Lessmann, Stefan & Voß, Stefan, 2013. "A memetic approach to construct transductive discrete support vector machines," European Journal of Operational Research, Elsevier, vol. 230(3), pages 581-595.

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