期刊
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 284, 期 1, 页码 273-284出版社
ELSEVIER
DOI: 10.1016/j.ejor.2019.12.007
关键词
Analytics; Churn prediction; Support vector machines; Minimax probability machine; Robust optimization
资金
- CONICYT PIA/BASAL [AFB180003]
- FONDECYT [1160738, 1160894]
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. (C) 2019 Elsevier B.V. All rights reserved.
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