@inproceedings{6fe19b4556854f06bf8c67e550d94865,
title = "Choosing the Right Time to Learn Evolving Data Streams",
abstract = "Continuous data generation over time presents new challenges for Machine Learning systems, which must develop real-time models due to memory and latency limitations. Streaming Machine Learning algorithms analyze data streams one sample at a time, progressively updating their models. However, is it necessary to utilize all the data for model updates? This paper introduces the Online Ensemble SPaced Learning (OE-SPL) strategy, an ensemble meta-strategy that combines online ensemble learning and the Spaced Learning heuristic to rapidly learn underlying concepts without using all samples. We evaluated OE-SPL on synthetic and real data streams containing various concept drifts, providing statistical evidence that OE-SPL achieves comparable performance to state-of-the-art ensemble models while recovering from multiple concept drift occurrences more efficiently, using less time and RAM-Hours.",
keywords = "Constrained Environment, Online Ensemble Learning, SML, Spaced Learning",
author = "Alessio Bernardo and Valle, \{Emanuele Della\} and Albert Bifet",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Big Data, BigData 2023 ; Conference date: 15-12-2023 Through 18-12-2023",
year = "2023",
month = jan,
day = "1",
doi = "10.1109/BigData59044.2023.10386551",
language = "English",
series = "Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5156--5165",
editor = "Jingrui He and Themis Palpanas and Xiaohua Hu and Alfredo Cuzzocrea and Dejing Dou and Dominik Slezak and Wei Wang and Aleksandra Gruca and Lin, \{Jerry Chun-Wei\} and Rakesh Agrawal",
booktitle = "Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023",
}