Workshop on Discovering Drift Phenomena in Evolving Data Landscape (DELTA)-Reference-Cited by-同舟云学术

Workshop on Discovering Drift Phenomena in Evolving Data Landscape (DELTA)

Published:2024-08-24 Issue: Volume: Page:6731-6732
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Piangerelli Marco¹^ORCID,Prenkaj Bardh²^ORCID,Rotalinti Ylenia³^ORCID,Joshi Ananya⁴^ORCID,Stilo Giovanni⁵^ORCID

Affiliation:

1. University of Camerino, Camerino, Italy

2. Technical University of Munich, Munich, Germany

3. Brunel University, London, United Kingdom

4. Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

5. Università of L' Aquila, L'Aquila, Italy

Funder

CDC/HHS

European Union - NextGenerationEU under the Italian Ministry of University and Re- search (MUR) National Innovation Ecosystem

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671492

Reference10 articles.

1. Oliver Cobb and Arnaud Van Looveren. 2022. Context-aware drift detection. In International Conference on Machine Learning. PMLR, 4087--4111.

2. Flavio Corradini, Caterina Luciani, Andrea Morichetta, and Marco Piangerelli. 2023. Managing Variability of Large Public Administration Event Log Collections: Dealing with Concept Drift. In International Conference on Business Informatics Research. Springer, 31--44.

3. Unsupervised concept drift detection for multi-label data streams

4. Fabian Hinder, Valerie Vaquet, and Barbara Hammer. [n.,d.]. One or Two Things We Know about Concept Drift--A Survey on Monitoring in Evolving Environments. Part A: Detecting Concept Drift. Frontiers in Artificial Intelligence, Vol. 7 ( [n.,d.]), 1330257.

5. Jie Lu, Anjin Liu, Fan Dong, Feng Gu, Joao Gama, and Guangquan Zhang. 2018. Learning under concept drift: A review. IEEE transactions on knowledge and data engineering, Vol. 31, 12 (2018), 2346--2363.