Blind Ensemble Classification of Sequential Data

Panagiotis A. Traganitis

doi:10.1109/DSW.2019.8755570

Blind Ensemble Classification of Sequential Data

Panagiotis A. Traganitis

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

2 Scopus citations

Abstract

The present work introduces a simple scheme for classifying sequential data using blind ensembles of classifiers. Blind refers to the combiner who has no knowledge of ground-truth labels to learn the optimal classifier combination. The sequence of data along with annotator responses are modeled using a hidden Markov model (HMM). The HMM parameters are learned using a decoupling and moment-matching approach. Preliminary tests on synthetic data showcase the potential of the proposed approach.

Original language	English (US)
Title of host publication	2019 IEEE Data Science Workshop, DSW 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	130-134
Number of pages	5
ISBN (Electronic)	9781728107080
DOIs	https://doi.org/10.1109/DSW.2019.8755570
State	Published - Jun 2019
Event	2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States Duration: Jun 2 2019 → Jun 5 2019

Publication series

Name	2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

Conference

Conference	2019 IEEE Data Science Workshop, DSW 2019
Country/Territory	United States
City	Minneapolis
Period	6/2/19 → 6/5/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

crowdsourcing
Ensemble learning
sequential data classification
unsupervised

Publisher link

10.1109/DSW.2019.8755570

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Traganitis, PA 2019, Blind Ensemble Classification of Sequential Data. in 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings., 8755570, 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 130-134, 2019 IEEE Data Science Workshop, DSW 2019, Minneapolis, United States, 6/2/19. https://doi.org/10.1109/DSW.2019.8755570

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keywords = "crowdsourcing, Ensemble learning, sequential data classification, unsupervised",

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year = "2019",

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AB - The present work introduces a simple scheme for classifying sequential data using blind ensembles of classifiers. Blind refers to the combiner who has no knowledge of ground-truth labels to learn the optimal classifier combination. The sequence of data along with annotator responses are modeled using a hidden Markov model (HMM). The HMM parameters are learned using a decoupling and moment-matching approach. Preliminary tests on synthetic data showcase the potential of the proposed approach.

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KW - Ensemble learning

KW - sequential data classification

KW - unsupervised

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M3 - Conference contribution

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BT - 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

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Y2 - 2 June 2019 through 5 June 2019

ER -

Blind Ensemble Classification of Sequential Data

Abstract

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Bibliographical note

Keywords

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