IDEAS home Printed from https://ideas.repec.org/r/eee/ejores/v223y2012i2p461-472.html
   My bibliography  Save this item

A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Narendra Singh & Pushpa Singh & Mukul Gupta, 2020. "An inclusive survey on machine learning for CRM: a paradigm shift," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 47(4), pages 447-457, December.
  2. Fan, Zhi-Ping & Sun, Minghe, 2016. "A multi-kernel support tensor machine for classification with multitype multiway data and an application to cross-selling recommendationsAuthor-Name: Chen, Zhen-Yu," European Journal of Operational Research, Elsevier, vol. 255(1), pages 110-120.
  3. 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.
  4. Liu, Zhenkun & Jiang, Ping & De Bock, Koen W. & Wang, Jianzhou & Zhang, Lifang & Niu, Xinsong, 2024. "Extreme gradient boosting trees with efficient Bayesian optimization for profit-driven customer churn prediction," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
  5. Yoon Sang Lee & Chulhwan Chris Bang, 2022. "Framework for the Classification of Imbalanced Structured Data Using Under-sampling and Convolutional Neural Network," Information Systems Frontiers, Springer, vol. 24(6), pages 1795-1809, December.
  6. 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.
  7. 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.
  8. Zhen-Yu Chen & Xin-Li Liu & Li-Ping Yin, 2023. "Data-driven product configuration improvement and product line restructuring with text mining and multitask learning," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 2043-2059, April.
  9. Höppner, Sebastiaan & Stripling, Eugen & Baesens, Bart & Broucke, Seppe vanden & Verdonck, Tim, 2020. "Profit driven decision trees for churn prediction," European Journal of Operational Research, Elsevier, vol. 284(3), pages 920-933.
  10. Mahajan, Pravar Dilip & Maurya, Abhinav & Megahed, Aly & Elwany, Alaa & Strong, Ray & Blomberg, Jeanette, 2020. "Optimizing predictive precision in imbalanced datasets for actionable revenue change prediction," European Journal of Operational Research, Elsevier, vol. 285(3), pages 1095-1113.
  11. Martínez, Andrés & Schmuck, Claudia & Pereverzyev, Sergiy & Pirker, Clemens & Haltmeier, Markus, 2020. "A machine learning framework for customer purchase prediction in the non-contractual setting," European Journal of Operational Research, Elsevier, vol. 281(3), pages 588-596.
  12. Dariusz Dudek & Marcin Lipowski & Ilona Bondos, 2021. "Changing Energy Supplier on the Market with a Strong Position of Incumbent Suppliers—Polish Example," Energies, MDPI, vol. 14(13), pages 1-16, June.
  13. Gary Mena & Kristof Coussement & Koen W. Bock & Arno Caigny & Stefan Lessmann, 2024. "Exploiting time-varying RFM measures for customer churn prediction with deep neural networks," Annals of Operations Research, Springer, vol. 339(1), pages 765-787, August.
  14. 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.
  15. Szeląg, Marcin & Słowiński, Roman, 2024. "Explaining and predicting customer churn by monotonic rules induced from ordinal data," European Journal of Operational Research, Elsevier, vol. 317(2), pages 414-424.
  16. Maldonado, Sebastián & López, Julio & Vairetti, Carla, 2020. "Profit-based churn prediction based on Minimax Probability Machines," European Journal of Operational Research, Elsevier, vol. 284(1), pages 273-284.
  17. Ram, Pappu Kalyan & Pandey, Neeraj & Persis, Jinil, 2024. "Modeling social coupon redemption decisions of consumers in food industry: A machine learning perspective," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
  18. Clemente-Císcar, M. & San Matías, S. & Giner-Bosch, V., 2014. "A methodology based on profitability criteria for defining the partial defection of customers in non-contractual settings," European Journal of Operational Research, Elsevier, vol. 239(1), pages 276-285.
  19. Jian Luo & Shu-Cherng Fang & Zhibin Deng & Xiaoling Guo, 2016. "Soft Quadratic Surface Support Vector Machine for Binary Classification," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(06), pages 1-22, December.
  20. Zhen-Yu Chen & Zhi-Ping Fan & Minghe Sun, 2014. "Ensemble Learning for Cross-Selling Using Multitype Multiway Data," Working Papers 0155mss, College of Business, University of Texas at San Antonio.
  21. Duan Lianjie, 2023. "Export Cutoff Productivity, Uncertainty and Duration of Waiting for Exporting," Economics - The Open-Access, Open-Assessment Journal, De Gruyter, vol. 17(1), pages 1-19, January.
  22. Muhammad Saeed Meo & Bezon Kumar & Sumayya Chughtai & Vina Javed Khan & Muhammad Khyzer Bin Dost & Qasim Ali Nisar, 2023. "Impact of Unemployment and Governance on Poverty in Pakistan: A Fresh Insight from Non-linear ARDL Co-integration Approach," Global Business Review, International Management Institute, vol. 24(5), pages 1007-1024, October.
  23. Maldonado, Sebastián & Domínguez, Gonzalo & Olaya, Diego & Verbeke, Wouter, 2021. "Profit-driven churn prediction for the mutual fund industry: A multisegment approach," Omega, Elsevier, vol. 100(C).
  24. Carrizosa, Emilio & Olivares-Nadal, Alba V. & Ramírez-Cobo, Pepa, 2013. "Time series interpolation via global optimization of moments fitting," European Journal of Operational Research, Elsevier, vol. 230(1), pages 97-112.
  25. Aimée Backiel & Bart Baesens & Gerda Claeskens, 2016. "Predicting time-to-churn of prepaid mobile telephone customers using social network analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(9), pages 1135-1145, September.
  26. Schaeffer, Satu Elisa & Rodriguez Sanchez, Sara Veronica, 2020. "Forecasting client retention — A machine-learning approach," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.