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Adaptive Ensemble Learning With Confidence Bounds

机译：具有置信范围的自适应合奏学习

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摘要

Extracting actionable intelligence from distributed, heterogeneous, correlated, and high-dimensional data sources requires run-time processing and learning both locally and globally. In the last decade, a large number of meta-learning techniques have been proposed in which local learners make online predictions based on their locally collected data instances, and feed these predictions to an ensemble learner, which fuses them and issues a global prediction. However, most of these works do not provide performance guarantees or, when they do, these guarantees are asymptotic. None of these existing works provide confidence estimates about the issued predictions or rate of learning guarantees for the ensemble learner. In this paper, we provide a systematic ensemble learning method called Hedged Bandits, which comes with both long-run (asymptotic) and short-run (rate of learning) performance guarantees. Moreover, our approach yields performance guarantees with respect to the optimal local prediction strategy, and is also able to adapt its predictions in a data-driven manner. We illustrate the performance of Hedged Bandits in the context of medical informatics and show that it outperforms numerous online and offline ensemble learning methods.
机译：从分布式，异构，相关和高维数据源中提取可操作的情报，需要在本地和全局进行运行时处理和学习。在过去的十年中，已经提出了许多元学习技术，其中本地学习者基于他们本地收集的数据实例进行在线预测，并将这些预测提供给整体学习者，后者将它们融合并发布全局预测。但是，这些作品大多数都不提供性能保证，或者当它们提供时，这些保证是渐近的。这些现有的著作都没有为整体学习者提供关于发布的预测或学习保证率的置信度估计。在本文中，我们提供了一种称为Hedged Bandits的系统集成学习方法，该方法同时具有长期（渐近）和短期（学习率）性能保证。此外，我们的方法相对于最佳局部预测策略可提供性能保证，并且还能够以数据驱动的方式调整其预测。我们在医疗信息学的背景下说明了对冲强盗的表现，并表明其胜过许多在线和离线合奏学习方法。

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