TSUNAMI - an explainable PPM approach for customer churn prediction in evolving retail data environments-Reference-Cited by-同舟云学术

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TSUNAMI - an explainable PPM approach for customer churn prediction in evolving retail data environments

Published:2023-12-28 Issue: Volume: Page:
ISSN:0925-9902
Container-title:Journal of Intelligent Information Systems
language:en
Short-container-title:J Intell Inf Syst

Author:

Pasquadibisceglie Vincenzo,Appice Annalisa,Ieva Giuseppe,Malerba Donato

Funder

LUTECH DIGITALE 4.0: Progetto di Tecniche di Machine Learning predittivo per la piattaforma di loyalty Management

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Information Systems,Software

Link

https://link.springer.com/content/pdf/10.1007/s10844-023-00838-5.pdf

Reference47 articles.

1. Ahn, J., Hwang, J., Kim, D., et al. (2020). A survey on churn analysis in various business domains. IEEE Access, 8, 220816–220839. https://doi.org/10.1109/ACCESS.2020.3042657

2. Alippi, C., Boracchi, G., & Roveri, M. (2017). Hierarchical change-detection tests. IEEE Transactions on Neural Networks and Learning Systems, 28(2), 246–258. https://doi.org/10.1109/TNNLS.2015.2512714

3. Alippi, C., & Roveri, M. (2008). Just-in-time adaptive classifiers-part i: Detecting nonstationary changes. IEEE Transactions on Neural Networks, 19(7), 1145–1153. https://doi.org/10.1109/TNN.2008.2000082

4. Benczúr, A. A., Kocsis, L., & Pálovics, R. (2019). Encyclopedia of big data technologies, chap. Online machine learning algorithms over data streams (pp. 1199–1207). Springer International Publishing: Cham. https://doi.org/10.1007/978-3-319-77525-8_329

5. Bifet, A., & Gavaldà, R. (2007). Learning from time-changing data with adaptive windowing. In: 7th SIAM International conference on data mining, proceedings (pp. 443–448). SIAM. https://doi.org/10.1137/1.9781611972771.42

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