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An important issue in ML consists in developing approaches exploiting background knowledge T for improving the accuracy and the robustness of learned classifiers C. Delegating the classification task to a Boolean circuit Σ exhibiting the same input-output behaviour as C, the problem of exploiting T within C can be viewed as a belief change scenario. However, usual change operations are not suited to the task of modifying the classifier encoding Σ in a minimal way, to make it complying with T. To fill the gap, we present a new belief change operation, called rectification. We characterize the family of rectification operators from an axiomatic perspective and exhibit operators from this family. We identify the standard belief change postulates that every rectification operator satisfies and those it does not. We also focus on some computational aspects of rectification and compliance.

On Belief Change for Multi-Label Classifier Encodings