LabelBoost: An Ensemble Model for Ground Truth Inference Using Boosted Trees
| Autor: | Siamak Faridani, Georg Buscher |
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| Rok vydání: | 2013 |
| Zdroj: | Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. 1:18-19 |
| ISSN: | 2769-1349 2769-1330 |
| DOI: | 10.1609/hcomp.v1i1.13134 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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