Profit-based churn prediction based on Minimax Probability Machines
| dc.coverage | DOI: 10.1016/j.ejor.2019.12.007 | |
| dc.creator | Maldonado, Sebastián | |
| dc.creator | López, Julio | |
| dc.creator | Vairetti, Carla | |
| dc.date | 2020 | |
| dc.date.accessioned | 05-01-2026 18:19 | |
| dc.date.available | 05-01-2026 18:19 | |
| dc.description | <p>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.</p> | eng |
| dc.identifier | https://investigadores.uandes.cl/en/publications/b64485fd-02c1-48df-9f71-6be5fdd3a405 | |
| dc.language | eng | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.source | vol.284 (2020) date: 2020-07-01 nr.1 p.273-284 | |
| dc.subject | Analytics | |
| dc.subject | Churn prediction | |
| dc.subject | Minimax probability machine | |
| dc.subject | Robust optimization | |
| dc.subject | Support vector machines | |
| dc.title | Profit-based churn prediction based on Minimax Probability Machines | eng |
| dc.type | Article | eng |
| dc.type | Artículo | spa |