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      • Department of Electrical and Electronics Engineering
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      Adaptive ensemble learning with confidence bounds

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      Author
      Tekin, C.
      Yoon, J.
      Schaar, M. V. D.
      Date
      2017
      Source Title
      IEEE Transactions on Signal Processing
      Print ISSN
      1053-587X
      Publisher
      Institute of Electrical and Electronics Engineers Inc.
      Volume
      65
      Issue
      4
      Pages
      888 - 903
      Language
      English
      Type
      Article
      Item Usage Stats
      109
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      91
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      Abstract
      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.
      Keywords
      Confidence bound
      Contextual bandits
      Ensemble learning
      Medical informatics
      Meta-learning
      Multi-armed bandits
      Online learning
      Regret
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      http://hdl.handle.net/11693/37096
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/TSP.2016.2626250
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