Meta-actions as a Tool for Action Rules Evaluation - Feature Selection for Data and Pattern Recognition - page 171

Chapter 9
Meta-actions as a Tool for Action
Rules Evaluation

Hakim Touati, Zbigniew W. Ras and James Studnicki

Abstract Action rules extraction is a field of data mining used to extract actionable
patterns from large datasets. Action rules present users with a set of actionable tasks
to follow to achieve a desired result. An action rule can be seen as two patterns of
feature values (classification rules) occurring together and having the same features.
Action rules are evaluated using their supporting patterns occurrence in a measure
called support. They are also evaluated using their confidence defined as the product
of the two patterns confidences. Those two measures are important to evaluate action
rules; nonetheless, they fail to measure the feature values transition correlation and
applicability. This is due to the core of the action rules extraction process that extracts
independent patterns and constructs an action rule. In this chapter, we present the
benefits of meta-actions in evaluating action rules in terms of two measures, namely
likelihood and execution confidence. In fact, in meta-actions, we extract real feature
values transition patterns, rather than composing two feature values patterns. We
also present an evaluation model of the application of meta-actions based on cost
and satisfaction. We extracted action rules and meta-actions and evaluated them
on the Florida State Inpatient Databases that is a part of the Healthcare Cost and
Utilization Project.