Post-processing a classifier's predictions: Strategies and empirical evaluation

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.

Authors

• Salem Benferhat
• Karim Tabia
• Mouaad Kezih
• Mahmoud Taibi

Keywords

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