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Proceedings Paper

Knowledge reduction algorithms based on rough set and conditional information entropy
Author(s): Hong Yu; Guoyin Wang; Dachun Yang; Zhongfu Wu
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Paper 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.

Paper Details

Date Published: 12 March 2002
PDF: 10 pages
Proc. SPIE 4730, Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV, (12 March 2002); doi: 10.1117/12.460205
Show Author Affiliations
Hong Yu, Chongqing Univ. and Chongqing Univ. of Posts and Telecommunications (China)
Guoyin Wang, Chongqing Univ. of Posts and Telecommunications (China)
Dachun Yang, ZTE Corp. (China)
Zhongfu Wu, Chongqing Univ. (China)

Published in SPIE Proceedings Vol. 4730:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV
Belur V. Dasarathy, Editor(s)

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