Discriminant versus Strong Rule Sets

摘要

The main objective of our research was to compare two completely different approaches to rule induction. In the first approach, represented by the LEM2 rule induction algorithm, induced rules are discriminant, i.e., every concept is completely described and rules are consistent. In the second approach, represented by the IRIM rule induction algorithm, a few strong and simple rules are induced. These rules do not necessarily completely describe concepts and, in general, are inconsistent. Though LEM2 frequently outperforms IRIM, the difference in performance is, statistically, insignificant. Thus IRIM, inducing a few strong but simple rules is a new and interesting addition to the LERS data mining system.