Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6894979 | European Journal of Operational Research | 2018 | 37 Pages |
Abstract
Churn prediction in telco remains a very active research topic. Due to the uptake of social network analytics and the results of previous benchmarking studies showing a rather flat maximum performance effect of predictive modeling techniques, the focus has mainly shifted to expanding and exploring the relevant feature space. While previous studies generally agree that adding features typically increases predictive performance, they rarely discuss the accompanying issues such as data availability and computational cost. In this work, we bridge the gap between predictive performance and operational efficiency by devising a new feature type classification and a novel reusable method to determine optimal feature type combinations based on Pareto multi-criteria optimization. Our results provide several insights that can serve as a guideline for industry practitioners.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Sandra Mitrović, Bart Baesens, Wilfried Lemahieu, Jochen De Weerdt,