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