Abstract
In this paper, we apply genetic rule selection to large-scale datasets. Since genetic
algorithms require many iterations of the computation, it is difficult to apply genetic rule selection
to large-scale datasets directly. In order to overcome this problem, we extend our genetic rule selection.
First, we partition a dataset into several subsets. Second, at each generation, we externally
store the nondominated solutions in terms of average fitness and survival times. Through computational
experiments, we show that genetic rule selection not only improves their classification
accuracy, but also significantly decreases the number of rules.
