Profit-based churn prediction based on Minimax Probability Machines

Sebastián Maldonado, Julio López, Carla Vairetti*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


In this paper, we propose three novel profit-driven strategies for churn prediction. Our proposals extend the ideas of the Minimax Probability Machine, a robust optimization approach for binary classification that maximizes sensitivity and specificity using a probabilistic setting. We adapt this method and other variants to maximize the profit of a retention campaign in the objective function, unlike most profit-based strategies that use profit metrics to choose between classifiers, and/or to define the optimal classification threshold given a probabilistic output. A first approach is developed as a learning machine that does not include a regularization term, and subsequently extended by including the LASSO and Tikhonov regularizers. Experiments on well-known churn prediction datasets show that our proposal leads to the largest profit in comparison with other binary classification techniques.

Original languageEnglish
Pages (from-to)273-284
Number of pages12
JournalEuropean Journal of Operational Research
Issue number1
StatePublished - 1 Jul 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2019 Elsevier B.V.


  • Analytics
  • Churn prediction
  • Minimax probability machine
  • Robust optimization
  • Support vector machines


Dive into the research topics of 'Profit-based churn prediction based on Minimax Probability Machines'. Together they form a unique fingerprint.

Cite this