Blind Ensemble Classification of Sequential Data

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

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 languageEnglish (US)
Title of host publication2019 IEEE Data Science Workshop, DSW 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages130-134
Number of pages5
ISBN (Electronic)9781728107080
DOIs
StatePublished - Jun 2019
Event2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States
Duration: Jun 2 2019Jun 5 2019

Publication series

Name2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

Conference

Conference2019 IEEE Data Science Workshop, DSW 2019
Country/TerritoryUnited States
CityMinneapolis
Period6/2/196/5/19

Bibliographical note

Funding Information:
Work in this paper was supported by NSF grants 1500713 and 1514056.

Publisher Copyright:
© 2019 IEEE.

Keywords

  • crowdsourcing
  • Ensemble learning
  • sequential data classification
  • unsupervised

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