Knowledge reduction algorithms based on rough set and conditional information entropy

Hong Yu; Guoyin Wang; Dachun Yang; Zhongfu Wu

doi:10.1117/12.460205

12 March 2002 Knowledge reduction algorithms based on rough set and conditional information entropy

Hong Yu, Guoyin Wang, Dachun Yang, Zhongfu Wu

Proceedings Volume 4730, Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV; (2002) https://doi.org/10.1117/12.460205
Event: AeroSense 2002, 2002, Orlando, FL, United States

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.

Citation Download Citation

Hong Yu, Guoyin Wang, Dachun Yang, and 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); https://doi.org/10.1117/12.460205

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