Mehdi Talebi - Faculty of Engineering Tarbiat Modares University Tehran, Iran
Mahdi Abadi - Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran, Iran

Artificial bee colony (ABC) is a new population-based algorithm that has shown promising results in the field of optimization. In this paper, we propose BeeMiner, a novel ABC algorithmfor discovering classification rules. BeeMiner differs from the original ABC because it uses an information-theoretic heuristicfunction (IHF) to guide the bees to search across the most promising areas of the search space. We compare the performanceof BeeMiner with those of J48, JRip, and PART on nine benchmark datasets from the UCI Machine Learning Repository. The results show that BeeMiner is competitive with J48, JRip, and PART in terms of the predictive accuracy

artificial bee colony; classification rule discovery;data mining; information theory