Blind multi-class ensemble learning with dependent classifiers

Panagiotis A. Traganitis, Georgios B Giannakis

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

4 Scopus citations


In recent years, advances in pattern recognition and data analytics have spurred the development of a plethora of machine learning algorithms and tools. However, as each algorithm exhibits different behavior for different types of data, 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 such a high-performance meta-learner, by combining the outputs from multiple algorithms. The present work introduces a simple blind scheme for learning from ensembles of classifiers. Blind refers to the combiner who has no knowledge of the ground-truth labels that each classifier has been trained on. While most current works presume that all classifiers are independent, this work introduces a scheme that can handle dependencies between classifiers. Preliminary tests on synthetic data showcase the potential of the proposed approach.

Original languageEnglish (US)
Title of host publication2018 26th European Signal Processing Conference, EUSIPCO 2018
PublisherEuropean Signal Processing Conference, EUSIPCO
Number of pages5
ISBN (Electronic)9789082797015
StatePublished - Nov 29 2018
Event26th European Signal Processing Conference, EUSIPCO 2018 - Rome, Italy
Duration: Sep 3 2018Sep 7 2018

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Other26th European Signal Processing Conference, EUSIPCO 2018

Bibliographical note

Funding Information:
Work in this paper was supported by NSF grants 1500713 and 1514056. Emails: {traga003,georgios} 1The terms annotator, learner, and classifier will be used interchangeably.

Publisher Copyright:
© EURASIP 2018.


  • Dependent classifiers
  • Ensemble learning
  • Multi-class classification
  • Unsupervised


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