Mining Actionable Patterns

摘要

We propose a generic framework that uses utility in decision making to drive the data mining process. We use concepts from meta-learning and build on earlier work by Elovici and Braha, that uses decision theory for formulating an utility measure, to specialize the framework for classification tasks. We show empirical validation of the approach on a simple test domain.