AAAI Publications, First AAAI Conference on Human Computation and Crowdsourcing

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LabelBoost: An Ensemble Model for Ground Truth Inference Using Boosted Trees
Siamak Faridani, Georg Buscher

Last modified: 2013-11-03


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.


Boosted Trees; Crowdsourcing; Label Aggregation

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