@inproceedings{7c46b544d22944c3a26f69e7b989901c,
title = "Reliable classification by unreliable crowds",
abstract = "We consider the use of error-control codes and decoding algorithms to perform reliable classification using unreliable and anonymous human crowd workers by adapting coding-theoretic techniques for the specific crowdsourcing application. We develop an ordering principle for the quality of crowds and describe how system performance changes with the quality of the crowd. We demonstrate the effectiveness of the proposed coding scheme using both simulated data and real datasets from Amazon Mechanical Turk, a crowdsourcing microtask platform. Results suggest that good codes may improve the performance of the crowdsourcing task over typical majority-vote approaches.",
keywords = "classification, crowdsourcing, error-control codes",
author = "Aditya Vempaty and Varshney, \{Lav R.\} and Varshney, \{Pramod K.\}",
year = "2013",
month = oct,
day = "18",
doi = "10.1109/ICASSP.2013.6638727",
language = "English",
isbn = "9781479903566",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "5558--5562",
booktitle = "2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings",
note = "2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 ; Conference date: 26-05-2013 Through 31-05-2013",
}