A Comparison of Rough Set Methods and Representative Inductive Learning Algorithms

Issue title: The 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Conputing (RSFDGrC 2003)

Article type: Research Article

Authors: Miao, Duoqian | Hou, Lishan

Affiliations: Department of Computer Science and Engineering, Tongji University, Shanghai 200092, P.R. China | Department of Mathematics, Shanxi University, Taiyuan 030006, P.R. China

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.

Keywords: rough sets, AQ algorithms, ID algorithms, attribute reduction, dynamic reduct

Journal: Fundamenta Informaticae, vol. 59, no. 2-3, pp. 203-219, 2004