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Due to the noises in crowdsourced labels, label aggregation (LA) has emergedas a standard procedure to post-process crowdsourced labels. LA methodsestimate true labels from crowdsourced labels by modeling worker qualities.Most existing LA methods are iterative in nature. They need to traverse all thecrowdsourced labels multiple times in order to jointly and iteratively updatetrue labels and worker qualities until convergence. Consequently, these methodshave high space and time complexities. In this paper, we treat LA as a dynamicsystem and model it as a Dynamic Bayesian network. From the dynamic model wederive two light-weight algorithms, LA\textsuperscript{onepass} andLA\textsuperscript{twopass}, which can effectively and efficiently estimateworker qualities and true labels by traversing all the labels at most twice.Due to the dynamic nature, the proposed algorithms can also estimate truelabels online without re-visiting historical data. We theoretically prove theconvergence property of the proposed algorithms, and bound the error ofestimated worker qualities. We also analyze the space and time complexities ofthe proposed algorithms and show that they are equivalent to those of majorityvoting. Experiments conducted on 20 real-world datasets demonstrate that theproposed algorithms can effectively and efficiently aggregate labels in bothoffline and online settings even if they traverse all the labels at most twice.

A Light-weight, Effective and Efficient Model for Label Aggregation in Crowdsourcing