Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/70805

To Treat, or Not to Treat: Reducing Volatility in Uplift Modeling Through Weighted Ensembles

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dc.contributor.author Rößler, Jannik
dc.contributor.author Tilly, Roman
dc.contributor.author Schoder, Detlef
dc.date.accessioned 2020-12-24T19:18:29Z
dc.date.available 2020-12-24T19:18:29Z
dc.date.issued 2021-01-05
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/70805
dc.description.abstract When conducting direct marketing activities, companies strive to know whom to target with a marketing incentive to maximize the campaign effect. For example, which customer should receive churn prevention incentive to minimize overall churn rate? Uplift modeling is a promising approach to answer such a question. It allows to separate customers who would likely react positively to a treatment from those who would remain neutral or even react negatively. However, while different uplift approaches have been proposed, they usually suffer from high volatility and their performance often depends largely on data set and application context. Thus, it is difficult for practitioners and researchers to apply uplift modeling. To overcome these problems, we propose a weighted ensemble of different uplift modeling approaches to reduce volatility and improve robustness. We evaluate the novel approach against single uplift modeling approaches on multiple data sets and find that the ensemble is indeed more robust.
dc.format.extent 10 pages
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Machine Learning and Predictive Analytics in Accounting, Finance, and Management
dc.subject customer targeting
dc.subject ensemble method
dc.subject predictive modeling
dc.subject uplift modeling
dc.title To Treat, or Not to Treat: Reducing Volatility in Uplift Modeling Through Weighted Ensembles
dc.identifier.doi 10.24251/HICSS.2021.193
prism.startingpage 1601
Appears in Collections: Machine Learning and Predictive Analytics in Accounting, Finance, and Management


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