Research Article

Improving Group Decision Support Systems using Rough Set

by  Mohamed Eisa
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Issue 2
Published: May 2013
Authors: Mohamed Eisa
10.5120/11812-7473
PDF

Mohamed Eisa . Improving Group Decision Support Systems using Rough Set. International Journal of Computer Applications. 69, 2 (May 2013), 9-13. DOI=10.5120/11812-7473

                        @article{ 10.5120/11812-7473,
                        author  = { Mohamed Eisa },
                        title   = { Improving Group Decision Support Systems using Rough Set },
                        journal = { International Journal of Computer Applications },
                        year    = { 2013 },
                        volume  = { 69 },
                        number  = { 2 },
                        pages   = { 9-13 },
                        doi     = { 10.5120/11812-7473 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2013
                        %A Mohamed Eisa
                        %T Improving Group Decision Support Systems using Rough Set%T 
                        %J International Journal of Computer Applications
                        %V 69
                        %N 2
                        %P 9-13
                        %R 10.5120/11812-7473
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, a proposed Group Decision Support Systems model based on Rough Set is presented. The model improves decision making process by using rough set as a tool for knowledge discovery on decision support system, where the same feature may evaluate by one decision maker as good and by another one as medium, in this case inconsistent will appear in decision problem. To cope with this problem, the model will be used to reduce inconsistent after computing lower and upper approximations. Moreover, the classification accuracy of the rough set with a single classifier and multiple classifiers was compared. These results indicate that, the model improve the classification accuracy for data sets, rather than using single and multiple classifiers.

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Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Group Decision Support Systems Rough set Classification accuracy

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