Personal context recognition via skeptical learning

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.