The theory of belief functions allows the fusion of imperfect data from different sources. Unfortunately, few real, imprecise and uncertain datasets exist to test approaches using belief functions. We have built real birds datasets thanks to the collection of numerous human contributions that we make available to the scientific community. The interest of our datasets is that they are made of human contributions, thus the information is therefore naturally uncertain and imprecise. These imperfections are given directly by the persons. This article presents the data and their collection through crowdsourcing and how to obtain belief functions from the data.

Classification is used to predict classes by extracting information from labeled data. But sometimes the collected data is imperfect, as in crowdsourcing where users have partial knowledge and may answer with uncertainty or imprecision. This paper offers a way to deal with uncertain and imprecise labeled data using Dempster-Shafer theory and active learning. An evidential version of K-NN that classifies a new example by observing its neighbors was earlier introduced. We propose to couple this approach with active learning, where the model uses only a fraction of the labeled data, and to compare it with non-evidential models. A new computable parameter for EK-NN is introduced, allowing the model to be both compatible with imperfectly labeled data and equivalent to its first version in the case of perfectly labeled data. This method increases the complexity but provides a way to work with imperfectly labeled data with efficient results and reduced labeling costs when coupled with active learning. We have conducted tests on real data imperfectly labeled during crowdsourcing campaigns.