Targeted Action Rule Discovery

The task of learning action rules aims to provide recommendations to analysts seeking to achieve a specific change. An action rule is constructed as a series of changes, or actions, which can be made to the flexible characteristics of a given object that ultimately triggers the desired change. Existing action rule discovery methods utilize a generate-and-test approach in which candidate action rules are generated and those that satisfy the user-defined thresholds are returned. A shortcoming of this operational model is there is no guarantee all objects are covered by the generated action rules. In this paper, we define a new methodology referred to as Targeted Action Rule Discovery (TARD). This methodology represents an object driven approach in which an action rule is explicitly discovered per target object. A TARD method is proposed that effectively discovers action rules through the iterative construction of multiple decision trees. Experiments show the proposed method is able to provide higher quality rules than the well-known Association Action Rule (AAR) method.