An extended genetic rule induction algorithm
J. Juan Liu, James Tin-Yau Kwok · 2002
Describes an extension of a genetic algorithm (GA) based separate-and-conquer propositional rule induction algorithm called SIA (Supervised Inductive Algorithm). 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 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 data sets from the UCI (University of California, Irvine) machine learning repository. The results show that the proposed system can achieve higher performance while still producing a smaller number of rules.