  Crowdsourcing has become very popular among the machine learning community as
a way to obtain labels that allow a ground truth to be estimated for a given
dataset. In most of the approaches that use crowdsourced labels, annotators are
asked to provide, for each presented instance, a single class label. Such a
request could be inefficient, that is, considering that the labelers may not be
experts, that way to proceed could fail to take real advantage of the knowledge
of the labelers. In this paper, the use of candidate labeling for crowd
learning is proposed, where the annotators may provide more than a single label
per instance to try not to miss the real label. The main hypothesis is that, by
allowing candidate labeling, knowledge can be extracted from the labelers more
efficiently by than in the standard crowd learning scenario. Empirical evidence
which supports that hypothesis is presented.
