Adaptive ensemble learning with confidence bounds

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dc.citation.epage903en_US
dc.citation.issueNumber4en_US
dc.citation.spage888en_US
dc.citation.volumeNumber65en_US
dc.contributor.authorTekin, C.en_US
dc.contributor.authorYoon, J.en_US
dc.contributor.authorSchaar, M. V. D.en_US
dc.date.accessioned2018-04-12T11:02:45Z
dc.date.available2018-04-12T11:02:45Z
dc.date.issued2017en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractExtracting 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.en_US
dc.identifier.doi10.1109/TSP.2016.2626250en_US
dc.identifier.issn1053-587X
dc.identifier.urihttp://hdl.handle.net/11693/37096
dc.language.isoEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/TSP.2016.2626250en_US
dc.source.titleIEEE Transactions on Signal Processingen_US
dc.subjectConfidence bounden_US
dc.subjectContextual banditsen_US
dc.subjectEnsemble learningen_US
dc.subjectMedical informaticsen_US
dc.subjectMeta-learningen_US
dc.subjectMulti-armed banditsen_US
dc.subjectOnline learningen_US
dc.subjectRegreten_US
dc.titleAdaptive ensemble learning with confidence boundsen_US
dc.typeArticleen_US
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