In this paper, we propose an approach allowing to revise the outputs of a classifier in order to take into account the available domain knowledge. This approach can be applied for any classifier be it probabilistic or not. We propose post-processing criteria and methods to encode and exploit different kinds of domain knowledge. Finally, we provide experimental studies on a set of benchmarks.
['Salem Benferhat', 'Karim Tabia', 'Mouaad Kezih', 'Mahmoud Taibi']
Post-processing a classifier's predictions: strategies and empirical evaluation