Self-assessment refers to the ability of a machine learning model to judge
the security of its classification. It constitutes a crucial requirement in safety
critical applications such as driver assistance systems, predictive maintenance of
plants, or medical classifications; further, self-assessment constitutes one crucial
property for classifier fusion. While confidence estimation performs reliably for
classical batch classification, their applicability is limited for complex machine
learning scenarios such as online learning models, learning scenarios which are
subject to drift, heterogeneous models, models which involve complex structured
data, or interactive models which incorporate human expertise.
The special session aims for contributions connected to the following non-
exhaustive list of topics:
confidence estimation in online learning techniques,
efficient rejection strategies based on adaptive confidence estimations,
self-assessment for heterogeneous classifier models,