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Communication Dans Un Congrès Année : 2020

On Ensemble Techniques for Data Stream Regression

Résumé

An ensemble of learners tends to exceed the predictive performance of individual learners. This approach has been explored for both batch and online learning. Ensembles methods applied to data stream classification were thoroughly investigated over the years, while their regression counterparts received less attention in comparison. In this work, we discuss and analyze several techniques for generating, aggregating, and updating ensembles of regressors for evolving data streams. We investigate the impact of different strategies for inducing diversity into the ensemble by randomizing the input data (resampling, random subspaces and random patches). On top of that, we devote particular attention to techniques that adapt the ensemble model in response to concept drifts, including adaptive window approaches, fixed periodical resets and randomly determined windows. Extensive empirical experiments show that simple techniques can obtain similar predictive performance to sophisticated algorithms that rely on reactive adaptation (i.e., concept drift detection and recovery).

Dates et versions

hal-04468443 , version 1 (20-02-2024)

Identifiants

Citer

Heitor Murilo Gomes, Jacob Montiel, Saulo Martiello Mastelini, Bernhard Pfahringer, Albert Bifet. On Ensemble Techniques for Data Stream Regression. 2020 International Joint Conference on Neural Networks, IJCNN 2020, Glasgow, United Kingdom, July 19-24, 2020, Jul 2020, Glasgow, United Kingdom. pp.1--8, ⟨10.1109/IJCNN48605.2020.9206756⟩. ⟨hal-04468443⟩
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