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Communication Dans Un Congrès Année : 2023

Choosing the Right Time to Learn Evolving Data Streams

Résumé

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.
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Dates et versions

hal-04468373 , version 1 (20-02-2024)

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Citer

Alessio Bernardo, Emanuele Della Valle, Albert Bifet. Choosing the Right Time to Learn Evolving Data Streams. 2023 IEEE International Conference on Big Data (BigData), Dec 2023, Sorrento, Italy. pp.5156--5165, ⟨10.1109/BIGDATA59044.2023.10386551⟩. ⟨hal-04468373⟩
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