Profit-based Churn Prediction based on Minimax Probability Machines
Abstract
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.
Más información
Título de la Revista: | EUROPEAN JOURNAL OF OPERATIONAL RESEARCH |
Volumen: | 284 |
Número: | 1 |
Editorial: | Elsevier |
Fecha de publicación: | 2020 |
Página de inicio: | 273 |
Página final: | 284 |
Idioma: | Ingles |
URL: | https://www.sciencedirect.com/science/article/abs/pii/S0377221719309919 |
DOI: |
10.1016/j.ejor.2019.12.007 |
Notas: | WOS core collection ISI |