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.
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Touati, H., Raś, Z.W., Studnicki, J. (2015). Meta-actions as a Tool for Action Rules Evaluation. In: Stańczyk, U., Jain, L. (eds) Feature Selection for Data and Pattern Recognition. Studies in Computational Intelligence, vol 584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45620-0_9
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DOI: https://doi.org/10.1007/978-3-662-45620-0_9
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