Action Rules Discovery Based on Tree Classifiers and 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. To our knowledge, all these algorithms assume that all attributes are symbolic or require prior discretization of all numerical attributes. This paper presents a new approach for generating action rules from datasets with numerical attributes by incorporating a tree classifier and a pruning step based on metaactions. Meta-actions are seen as a higher-level knowledge (provided by experts) about correlations between different attributes.