Abstract
In personal context recognition many solutions rely
on supervised learning that uses sensor data collected from the users’ mobile devices. However,
the recognition performance is significantly affected by the annotations’ quality. The problem
lies in the fact that the annotator in such scenarios is usually the user herself which is not an expert
and thus provides a significant amount of incorrect
labels, while existing solutions can only tolerate a
small fraction of mislabels. Our solution is skeptical learning, a framework for interactive machine
learning where the machine uses all its available
knowledge to check the correctness of its own and
the user labeling. This allows us to have a uniform
confidence measure to be used when a contradiction arises that applies to both the annotator and the
machine. The criteria of success is an improvement
of the final recognition accuracy with respect to traditional supervised approaches