  Crowdsourcing is the outsourcing of tasks to a crowd of contributors on a
dedicated platform. The crowd on these platforms is very diversified and
includes various profiles of contributors which generates data of uneven
quality. However, majority voting, which is the aggregating method commonly
used in platforms, gives equal weight to each contribution. To overcome this
problem, we propose a method, MONITOR, which estimates the contributor's
profile and aggregates the collected data by taking into account their possible
imperfections thanks to the theory of belief functions. To do so, MONITOR
starts by estimating the profile of the contributor through his qualification
for the task and his behavior.Crowdsourcing campaigns have been carried out to
collect the necessary data to test MONITOR on real data in order to compare it
to existing approaches. The results of the experiments show that thanks to the
use of the MONITOR method, we obtain a better rate of correct answer after
aggregation of the contributions compared to the majority voting. Our
contributions in this article are for the first time the proposal of a model
that takes into account both the qualification of the contributor and his
behavior in the estimation of his profile. For the second one, the weakening
and the aggregation of the answers according to the estimated profiles.
