Learning from unequally reliable blind ensembles of classifiers

Panagiotis A. Traganitis; Alba Pagès-Zamora; Georgios B Giannakis

doi:10.1109/GlobalSIP.2017.8308613

Learning from unequally reliable blind ensembles of classifiers

Panagiotis A. Traganitis, Alba Pagès-Zamora, Georgios B Giannakis

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 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 language	English (US)
Title of host publication	2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	106-110
Number of pages	5
ISBN (Electronic)	9781509059904
DOIs	https://doi.org/10.1109/GlobalSIP.2017.8308613
State	Published - Mar 7 2018
Event	5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Montreal, Canada Duration: Nov 14 2017 → Nov 16 2017

Publication series

Name	2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
Volume	2018-January

Other

Other	5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017
Country/Territory	Canada
City	Montreal
Period	11/14/17 → 11/16/17

Bibliographical note

Funding Information:
Work supported by NSF grants 1500713 and 1514056, as well as by "Ministerio de Economía y Competitividad" of the Spanish Government, ERDF funds (TEC2013-41315-R,TEC2015-69648-REDC,TEC2016-75067-C4-2-R).

Funding Information:
Work supported by NSF grants 1500713 and 1514056, as well as by “Min-isterio de Economía y Competitividad” of the Spanish Government, ERDF funds (TEC2013-41315-R,TEC2015-69648-REDC,TEC2016-75067-C4-2-R) Emails: {traga003@umn.edu, alba.pages@upc.edu, georgios@umn.edu} 1The terms annotator, learner, and classifier will be used interchangeably.

Publisher Copyright:
© 2017 IEEE.

Keywords

Ensemble learning
multi-class classification
unsupervised

Publisher link

10.1109/GlobalSIP.2017.8308613

Find It

Find It at U of M Libraries

Cite this

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

Learning from unequally reliable blind ensembles of classifiers. / Traganitis, Panagiotis A.; Pagès-Zamora, Alba; Giannakis, Georgios B.
2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 106-110 (2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Traganitis, PA, Pagès-Zamora, A & Giannakis, GB 2018, Learning from unequally reliable blind ensembles of classifiers. in 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings. 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 106-110, 5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017, Montreal, Canada, 11/14/17. https://doi.org/10.1109/GlobalSIP.2017.8308613

Traganitis PA, Pagès-Zamora A, Giannakis GB. Learning from unequally reliable blind ensembles of classifiers. In 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 106-110. (2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings). doi: 10.1109/GlobalSIP.2017.8308613

Traganitis, Panagiotis A. ; Pagès-Zamora, Alba ; Giannakis, Georgios B. / Learning from unequally reliable blind ensembles of classifiers. 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 106-110 (2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings).

@inproceedings{6f80507b15d34d37b807f30236ec1204,

title = "Learning from unequally reliable blind ensembles of classifiers",

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.",

keywords = "Ensemble learning, multi-class classification, unsupervised",

author = "Traganitis, {Panagiotis A.} and Alba Pag{\`e}s-Zamora and Giannakis, {Georgios B}",

note = "Funding Information: Work supported by NSF grants 1500713 and 1514056, as well as by {"}Ministerio de Econom{\'i}a y Competitividad{"} of the Spanish Government, ERDF funds (TEC2013-41315-R,TEC2015-69648-REDC,TEC2016-75067-C4-2-R). Funding Information: Work supported by NSF grants 1500713 and 1514056, as well as by “Min-isterio de Econom{\'i}a y Competitividad” of the Spanish Government, ERDF funds (TEC2013-41315-R,TEC2015-69648-REDC,TEC2016-75067-C4-2-R) Emails: {traga003@umn.edu, alba.pages@upc.edu, georgios@umn.edu} 1The terms annotator, learner, and classifier will be used interchangeably. Publisher Copyright: {\textcopyright} 2017 IEEE.; 5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 ; Conference date: 14-11-2017 Through 16-11-2017",

year = "2018",

month = mar,

day = "7",

doi = "10.1109/GlobalSIP.2017.8308613",

language = "English (US)",

series = "2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "106--110",

booktitle = "2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings",

}

TY - GEN

T1 - Learning from unequally reliable blind ensembles of classifiers

AU - Traganitis, Panagiotis A.

AU - Pagès-Zamora, Alba

AU - Giannakis, Georgios B

N1 - Funding Information: Work supported by NSF grants 1500713 and 1514056, as well as by "Ministerio de Economía y Competitividad" of the Spanish Government, ERDF funds (TEC2013-41315-R,TEC2015-69648-REDC,TEC2016-75067-C4-2-R). Funding Information: Work supported by NSF grants 1500713 and 1514056, as well as by “Min-isterio de Economía y Competitividad” of the Spanish Government, ERDF funds (TEC2013-41315-R,TEC2015-69648-REDC,TEC2016-75067-C4-2-R) Emails: {traga003@umn.edu, alba.pages@upc.edu, georgios@umn.edu} 1The terms annotator, learner, and classifier will be used interchangeably. Publisher Copyright: © 2017 IEEE.

PY - 2018/3/7

Y1 - 2018/3/7

N2 - 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.

AB - 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.

KW - Ensemble learning

KW - multi-class classification

KW - unsupervised

UR - http://www.scopus.com/inward/record.url?scp=85048126467&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85048126467&partnerID=8YFLogxK

U2 - 10.1109/GlobalSIP.2017.8308613

DO - 10.1109/GlobalSIP.2017.8308613

M3 - Conference contribution

AN - SCOPUS:85048126467

T3 - 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings

SP - 106

EP - 110

BT - 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017

Y2 - 14 November 2017 through 16 November 2017

ER -

Learning from unequally reliable blind ensembles of classifiers

Abstract

Publication series

Other

Bibliographical note

Keywords

Publisher link

Find It

Other files and links

Fingerprint

Cite this