A comparison of rough set methods and representative inductive learning algorithms

Authors:
Duoqian Miao;Lishan Hou
Affiliations:
Department of Computer Science and Engineering, Tongji University, Shanghai 200092, P R. China;Department of Mathematics, Shanxi University, Taiyuan 030006, P.R. China
Venue:
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
Year:
2003

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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, we discuss some inductive machine learning techniques in the framework of the knowledge reduction approach based on rough set theory. The Monk's problems introduced in the early of nineties are resolved again employing rough set methods and their results are compared and analyzed with those obtained at that time. As far as accuracy and conciseness are concerned, the learning algorithms based on rough sets have remarkable superiority.