Adaptive ensemble learning with confidence bounds
Author
Tekin, C.
Yoon, J.
Schaar, M. V. D.
Date
2017Source 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
ArticleItem Usage Stats
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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 boundContextual bandits
Ensemble learning
Medical informatics
Meta-learning
Multi-armed bandits
Online learning
Regret