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.

Published Date: 2013-11-10
Registration: ISBN 978-1-57735-607-3
DOI:
10.1609/hcomp.v1i1.13134