A Comparison of Rough Set Methods and Representative Inductive Learning Algorithms

Abstract

Rough set theory is a kind of new tool to deal with knowledge, particularly when knowledge is imprecise, inconsistent and incomplete. In this paper, the main techniques of inductive machine learning are united to the knowledge reduction theory based on rough sets from the theoretical point of view. The Monk's problems introduced in the early of nineties are resolved again employing rough sets and their results are analyzed and compared with those of that time. As far as accuracy and conciseness are concerned, the learning algorithms based on rough sets have remarkable superiority.