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<p>Ensemble methods for stream mining necessitate managing multiple models and updating them as data distributions evolve. Considering the calls for more sustainability, established methods are however not sufficiently considerate of ensemble members’ computational expenses and instead overly focus on predictive capabilities. To address these challenges and enable green online learning, we propose <i>heterogeneous</i> <i>online ensembles</i> (HEROS). For every training step, HEROS chooses a subset of models from a pool of models initialized with diverse hyperparameter choices under resource constraints to train. We introduce a Markov decision process to theoretically capture the trade-offs between predictive performance and sustainability constraints. Based on this framework, we present different policies for choosing which models to train on incoming data. Most notably, we propose the novel <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\zeta \)</EquationSource> </InlineEquation>-policy, which focuses on training near-optimal models at reduced costs. Using a stochastic model, we theoretically prove that our <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\zeta \)</EquationSource> </InlineEquation>-policy achieves near optimal performance while using fewer resources compared to the best performing policy. In our experiments across 11 benchmark datasets, we find empiric evidence that our <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\zeta \)</EquationSource> </InlineEquation>-policy is a strong contribution to the state-of-the-art, demonstrating highly accurate performance, in some cases even outperforming competitors, and simultaneously being much more resource-friendly.</p>

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Lift what you can: green online learning with heterogeneous ensembles

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月24日

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  • Kirsten Köbschall,
  • Sebastian Buschjäger,
  • Raphael Fischer,
  • Lisa Hartung,
  • Stefan Kramer

摘要

Ensemble methods for stream mining necessitate managing multiple models and updating them as data distributions evolve. Considering the calls for more sustainability, established methods are however not sufficiently considerate of ensemble members’ computational expenses and instead overly focus on predictive capabilities. To address these challenges and enable green online learning, we propose heterogeneous online ensembles (HEROS). For every training step, HEROS chooses a subset of models from a pool of models initialized with diverse hyperparameter choices under resource constraints to train. We introduce a Markov decision process to theoretically capture the trade-offs between predictive performance and sustainability constraints. Based on this framework, we present different policies for choosing which models to train on incoming data. Most notably, we propose the novel \(\zeta \) -policy, which focuses on training near-optimal models at reduced costs. Using a stochastic model, we theoretically prove that our \(\zeta \) -policy achieves near optimal performance while using fewer resources compared to the best performing policy. In our experiments across 11 benchmark datasets, we find empiric evidence that our \(\zeta \) -policy is a strong contribution to the state-of-the-art, demonstrating highly accurate performance, in some cases even outperforming competitors, and simultaneously being much more resource-friendly.