Raykar, Vikas and Yu, Shipeng and Zhao, Linda and Jerebko, Anna and Florin, Charles and Valadez, Gerardo and Bogoni, Luca and Moy, Linda
We describe a probabilistic approach for supervised learning when we have multiple experts/annotators providing (possibly noisy) labels but no absolute gold standard. The proposed algorithm evaluates the different experts and also gives an estimate of the actual hidden labels. Experimental results indicate that the proposed method clearly beats the commonly used majority voting baseline.