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 level
