Classification Rule Mining with Iterated Greedy

Abstract

In the context of data mining, classi cation rule discovering is the task of designing accurate rule based systems that model the useful knowledge that di erentiate some data classes from others, and is present in large data sets. Iterated greedy search is a powerful metaheuristic, successfully applied to di erent optimisation problems, which to our knowledge, has not previously been used for classi cation rule mining. In this work, we analyse the convenience of using iterated greedy algorithms for the design of rule classi cation systems. We present and study di erent alternatives and compare the results with state-of-the-art methodologies from the literature. The results show that iterated greedy search may generate accurate rule classi cation systems with acceptable interpretability levels