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Learning from unequally reliable blind ensembles of classifiers

Author
Traganitis, P. A.; Pagès-Zamora, A.; Giannakis, G.B.
Type of activity
Presentation of work at congresses
Name of edition
5th IEEE Global Conference on Signal and Information Processing
Date of publication
2017
Presentation's date
2017-11-15
Book of congress proceedings
2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP 2017): Montreal, Quebec, Canada: 14-16 November 2017
First page
106
Last page
110
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
DOI
https://doi.org/10.1109/GlobalSIP.2017.8308613 Open in new window
Project funding
Distributed techniques for the management and operation of wireless cellular networks, sensor networks and the smart energy grid
Red COMONSENS
TEC2016-75067-C4-2-R - CARMEN
Repository
http://hdl.handle.net/2117/117837 Open in new window
URL
https://ieeexplore.ieee.org/document/8308613/ Open in new window
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...
Citation
Traganitis, P. A., Pagès-Zamora, A., Giannakis, G.B. Learning from unequally reliable blind ensembles of classifiers. A: IEEE Global Conference on Signal and Information Processingg. "2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP 2017): Montreal, Quebec, Canada: 14-16 November 2017". Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 106-110.
Keywords
Ensemble learning, Ensembles of classifiers, Highperformance, Joint matrix factorizations, Learning algorithms, Learning systems, Meta-algorithms, Multi-class classification, Multiple algorithms, Pattern recognition, multi-class classification, unsupervised, unsupervised Factorization
Group of research
SPCOM - Signal Processing and Communications Group

Participants

  • Traganitis, Panagiotis  (author and speaker )
  • Pages Zamora, Alba Maria  (author and speaker )
  • Giannakis, Georgios B.  (author and speaker )

Attachments