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Optimal Inference in Crowdsourced Classification via Belief Propagation

机译：基于信念传播的众包分类的最佳推理

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摘要

Crowdsourcing systems are popular for solving large-scale labeling tasks with low-paid workers. We study the problem of recovering the true labels from the possibly erroneous crowdsourced labels under the popular Dawid-Skene model. To address this inference problem, several algorithms have recently been proposed, but the best known guarantee is still significantly larger than the fundamental limit. We close this gap by introducing a tighter lower bound on the fundamental limit and proving that the belief propagation (BP) exactly matches the lower bound. The guaranteed optimality of BP is the strongest in the sense that it is information-theoretically impossible for any other algorithm to correctly label a larger fraction of the tasks. Experimental results suggest that the BP is close to optimal for all regimes considered and improves upon competing the state-of-the-art algorithms.
机译：众包系统非常适合解决低薪工人的大规模贴标任务。我们研究了在流行的Dawid-Skene模型下从可能错误的众包标签中恢复真实标签的问题。为了解决这个推论问题，最近提出了几种算法，但是最著名的保证仍然明显大于基本极限。我们通过在基本限制上引入更严格的下限并证明信念传播（BP）与下限完全匹配来弥合这一差距。从理论上讲，任何其他算法都无法正确标记较大部分任务，因此从理论上讲，BP的最优保证是最强的。实验结果表明，对于所有考虑的方案，BP都接近最佳状态，并且在与最新算法竞争时得到了改进。

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