Attribute augmentation-based label integration for crowdsourcing

Crowdsourcing provides an effective and low-cost way to collect labels from crowd workers. Due to the lack of professional knowledge, the quality of crowdsourced labels is relatively low. A common approach to addressing this issue is to collect multiple labels for each instance from different crowd workers and then a label integration method is used to infer its true label. However, almost all existing label integration methods merely make use of the original attribute information and do not pay attention …

augmentation computer sciences cost crowdsourcing instance integration issue knowledge labels low multiple professional quality workers