12 March 2002 Knowledge reduction algorithms based on rough set and conditional information entropy
Author Affiliations +
Abstract
Rough Set is a valid mathematical theory developed in recent years, which has the ability to deal with imprecise, uncertain, and vague information. It has been applied in such fields as machine learning, data mining, intelligent data analyzing and control algorithm acquiring successfully. Many researchers have studied rough sets in different view. In this paper, the authors discuss the reduction of knowledge using information entropy in rough set theory. First, the changing tendency of the conditional entropy of decision attributes given condition attributes is studied from the viewpoint of information. Then, two new algorithms based on conditional entropy are developed. These two algorithms are analyzed and compared with MIBARK algorithm. Furthermore, our simulation results show that the algorithms can find the minimal reduction in most cases.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hong Yu, Hong Yu, Guoyin Wang, Guoyin Wang, Dachun Yang, Dachun Yang, Zhongfu Wu, Zhongfu Wu, } "Knowledge reduction algorithms based on rough set and conditional information entropy", Proc. SPIE 4730, Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV, (12 March 2002); doi: 10.1117/12.460205; https://doi.org/10.1117/12.460205
PROCEEDINGS
10 PAGES


SHARE
Back to Top