LabelBoost: An Ensemble Model for Ground Truth Inference Using Boosted Trees
DOI:
https://doi.org/10.1609/hcomp.v1i1.13134Keywords:
Boosted Trees, Crowdsourcing, Label AggregationAbstract
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.
Downloads
Published
2013-11-03
How to Cite
Faridani, S., & Buscher, G. (2013). LabelBoost: An Ensemble Model for Ground Truth Inference Using Boosted Trees. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 1(1), 18-19. https://doi.org/10.1609/hcomp.v1i1.13134
Issue
Section
Works in Progress