Learning from unequally reliable blind ensembles of classifiers

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The rising interest in pattern recognition and data analytics has spurred the development of a plethora of machine learning algorithms and tools. However, as each algorithm has its strengths and weaknesses, one is motivated to judiciously fuse multiple algorithms in order to find the 'best' performing one, for a given dataset. Ensemble learning aims to create a highperformance meta-algorithm, by combining the outputs from multiple algorithms. The present work introduces a simple blind scheme for learning from ensembles of classifiers, using joint matrix factorization. Blind refers to the combiner who has no knowledge of the ground-truth labels that each classifier has been trained on. Performance is evaluated on synthetic and real datasets.

Original languageEnglish (US)
Title of host publication2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages106-110
Number of pages5
ISBN (Electronic)9781509059904
DOIs
StatePublished - Mar 7 2018
Event5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Montreal, Canada
Duration: Nov 14 2017Nov 16 2017

Publication series

Name2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
Volume2018-January

Other

Other5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017
CountryCanada
CityMontreal
Period11/14/1711/16/17

Keywords

  • Ensemble learning
  • multi-class classification
  • unsupervised

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    Traganitis, P. A., Pagès-Zamora, A., & Giannakis, G. B. (2018). Learning from unequally reliable blind ensembles of classifiers. In 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings (pp. 106-110). (2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2017.8308613