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.|
|Number of pages||5|
|State||Published - Jun 2019|
|Event||2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States|
Duration: Jun 2 2019 → Jun 5 2019
|Name||2019 IEEE Data Science Workshop, DSW 2019 - Proceedings|
|Conference||2019 IEEE Data Science Workshop, DSW 2019|
|Period||6/2/19 → 6/5/19|
Bibliographical noteFunding Information:
Work in this paper was supported by NSF grants 1500713 and 1514056.
© 2019 IEEE.
- Ensemble learning
- sequential data classification