Abstract: This paper describes an extension of a GA-based, separate-and-conquer propositional rule induction algorithm called SIA. While the original algorithm is computationally attractive and is also able to handle both nominal and continuous attributes efficiently, our algorithm further improves it by taking into account of the recent advances in the rule induction and evolutionary computation communities. The refined system has been compared to other GA-based and non GA-based rule learning algorithms on a number of benchmark datasets from the UCI machine learning repository. Results show that the proposed system can achieve higher performance while still produces a smaller number of rules.
Proceedings of the Congress on Evolutionary Computation (CEC), pp.458-463, La Jolla, California, USA, July 2000.