A Multi-Objective Evolutionary Action Rule Mining Method

Abstract

Action rules are rules that describe how to transition a decision attribute from an undesired state to a desired state, with the understanding that some attributes are stable and others are flexible. Stable attributes, such as “age”, may not be changed, whereas flexible attributes, such as “interest rate”, may be changed. Action rules have great potential in data mining, as they output easily interpretable rules which can immediately be useful to a decision maker. However, at present, the methods to generate all valid action rules are computationally expensive. To address this, methods have been proposed that prune swaths of the search space as rules are generated; this results in computational efficiency, at the expense of potentially not discovering many useful rules. In this work, a method, called Multi-Objective Evolutionary Action Rule (MOEAR) mining, is introduced. MOEAR optimizes the discovery of action rules using standard evolutionary algorithm principles. Experimental results show that MOEAR is able to generate a large number of potentially interesting action rules, including those rules that could be categorized as “rare”, while achieving good computational performance.