Abstract:
This paper describes methods for reasoning with missing, irrelevant and not applicable meta-values in the AQ attributional rule learning. The methods address issues of handling these values in datasets both for rule learning and rule testing. In rule learning, the presence of these values affects the extension-against generalization operator in star generation, and the rule matching operator. In rule testing, these values affect the execution of the rule matching operator. The presented methods have been implemented in the AQ21 learning program and tested on four datasets.
