Selective sampling of labelers for approximating the crowd

Date

2012-11-05

Author

Ertekin Bolelli, Şeyda
Rudin, Cynthia

Metadata

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In this paper, we present CrowdSense, an algorithm for estimating the crowd’s majority opinion by querying only a subset of it. CrowdSense works in an online fashion where examples come one at a time and it dynamically samples 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. We also present two probabilistic variants of CrowdSense that are based on different assumptions on the joint probability distribution between the labelers’ votes and the majority vote. Our experiments demonstrate that we can reliably approximate the entire crowd’s vote by collecting opinions from a representative subset of the crowd.

Subject Keywords

Crowdsourcing, Wisdom of crowds, Labeler quality estimation, Approximating the crowd, Aggregating the opinions

URI

https://www.aaai.org/ocs/index.php/FSS/FSS12/paper/view/5596
https://hdl.handle.net/11511/87375
https://www.scopus.com/record/display.uri?eid=2-s2.0-84875587840&origin=resultslist&sort=plf-f&src=s&st1=&st2=&sid=3737d5751df2767d801d254a036d826a&sot=b&sdt=b&sl=74&s=TITLE-ABS-KEY+%28Selective+sampling+of+labelers+for+approximating+the+crowd%29&relpos=0&citeCnt=1&searchTerm=

Conference Name

2012 AAAI Fall Symposium

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

Ş. Ertekin Bolelli and C. Rudin, “Selective sampling of labelers for approximating the crowd,” Arlington, VA; United States, 2012, p. 7, Accessed: 00, 2021. [Online]. Available: https://www.aaai.org/ocs/index.php/FSS/FSS12/paper/view/5596.