Abstract
In this chapter, the use of a method for attribute selection in a dispersed decision-making system is discussed. Dispersed knowledge is understood to be the knowledge that is stored in the form of several decision tables. Different methods for solving the problem of classification based on dispersed knowledge are considered. In the first method, a static structure of the system is used. In more advanced techniques, a dynamic structure is applied. Different types of dynamic structures are analyzed: a dynamic structure with disjoint clusters, a dynamic structure with inseparable clusters and a dynamic structure with negotiations. A method for attribute selection, which is based on the rough set theory, is used in all of the methods described here. The results obtained for five data sets from the UCI Repository are compared and some conclusions are drawn.
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.
