Learning to Predict the Wisdom of Crowds
Social and Information Networks
2012-04-17 v1 Machine Learning
Abstract
The problem of "approximating the crowd" is that of estimating the crowd's majority opinion by querying only a subset of it. Algorithms that approximate the crowd can intelligently stretch a limited budget for a crowdsourcing task. We present an algorithm, "CrowdSense," that works in an online fashion to dynamically sample subsets of labelers based on an exploration/exploitation criterion. The algorithm produces a weighted combination of a subset of the labelers' votes that approximates the crowd's opinion.
Cite
@article{arxiv.1204.3611,
title = {Learning to Predict the Wisdom of Crowds},
author = {Seyda Ertekin and Haym Hirsh and Cynthia Rudin},
journal= {arXiv preprint arXiv:1204.3611},
year = {2012}
}
Comments
Presented at Collective Intelligence conference, 2012 (arXiv:1204.2991)