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Communication Dans Un Congrès Année : 2023

FALL: A Modular Adaptive Learning Platform for Streaming Data

Résumé

A growing number of tasks require adaptive machine learning systems capable of learning continuously from incoming data and adapting to changes in their environment. In order to enable the widespread adoption of machine learning for streaming data, it is crucial that practitioners and researchers have the tools to efficiently build and evaluate adaptive learning systems. In this paper we demonstrate FALL, a Framework for Adaptive Life-long Learning, which we have developed to enable the full adaptive learning pipeline to be built using modular, reusable components, enabling users to easily and efficiently develop, implement, and evaluate state-of-the-art adaptive learning systems. Source code, documentation, and examples may be found at https://benhalstead.dev/FALL/.
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Dates et versions

hal-04468378 , version 1 (20-02-2024)

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Citer

Ben Halstead, Yun Sing Koh, Patricia Riddle, Mykola Pechenizkiy, Albert Bifet. FALL: A Modular Adaptive Learning Platform for Streaming Data. 39th IEEE International Conference on Data Engineering, ICDE 2023, Anaheim, CA, USA, April 3-7, 2023, Apr 2023, California, United States. pp.3619--3622, ⟨10.1109/ICDE55515.2023.00282⟩. ⟨hal-04468378⟩
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