Abstract
Although many extended rough set models have been successfully applied into the incomplete information system, most of them do not take the incomplete information system with initial fuzzy data into account. This paper thus presents a general framework for the study of dominance-based rough set approach to the incomplete fuzzy information systems. First, the traditional dominance relation is expanded in the incomplete fuzzy information system by assuming the condition attributes, which are not only positively but also negatively related with classification analysis. We then present the dominance-based rough approximations by the rough fuzzy technique. Finally, we propose two types of knowledge reductions, relative lower and upper approximate reducts, which can be used to induce simplified decision rules from the incomplete fuzzy decision table. We also present the judgement theorems and discernibility functions, which describe how relative lower and upper approximate reducts can be calculated. We employ some numerical examples in this paper to substantiate the conceptual arguments.
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 callMore From: International Journal of Granular Computing, Rough Sets and Intelligent Systems
Disclaimer: 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.
