Published:
2013-11-10
Proceedings:
Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 1
Volume
Issue:
Vol. 1 (2013): First AAAI Conference on Human Computation and Crowdsourcing
Track:
Works in Progress
Downloads:
Abstract:
We introduce LabelBoost, an ensemble model that utilizes various label aggregation algorithms to build a higher precision algorithm. We compare this algorithm with majority vote, GLAD and an Expectation Maximization model on a publicly available dataset. The results suggest that by building an ensemble model, one can achieve higher precision value for aggregating crowd-sourced labels for an item. These higher values are shown to be statistically significant.
DOI:
10.1609/hcomp.v1i1.13134
![](https://aaai.org/wp-content/uploads/2023/04/cover_issue_315_en_US.jpg)
HCOMP
Vol. 1 (2013): First AAAI Conference on Human Computation and Crowdsourcing
ISBN 978-1-57735-607-3