Support confidence and utility of action rules triggered by meta-actions

Action rules describe possible transitions of objects from one state to another with respect to a distinguished attribute. Early research on action rule discovery usually required the extraction of classification rules before constructing any action rule. Newest algorithms discover action rules directly from a decision system. We employ a pruning step in action rule generation, through the use of meta-actions. They are nodes of higher-level knowledge, linked with atomic action terms, which show changes triggered within classification attributes. In this paper, we propose improved measures for support and confidence of action rules, as well as we introduce a new measure - the notion of utility of action rules. We perform an experiment in medical domain using Mammographic Mass dataset, where action rules suggest possible ways to re-classify breast tumors from malignant to benign severity class. Results show increased support and confidence for the new proposed measures compared to the standard measures.