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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
