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Improving Group Decision Support Systems using Rough Set

by Mohamed Eisa
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 2
Year of Publication: 2013
Authors: Mohamed Eisa
10.5120/11812-7473

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 },
issue_date = { May 2013 },
volume = { 69 },
number = { 2 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 9-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number2/11812-7473/ },
doi = { 10.5120/11812-7473 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:29:10.051281+05:30
%A Mohamed Eisa
%T Improving Group Decision Support Systems using Rough Set
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 2
%P 9-13
%D 2013
%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.

References
  1. Gray, P. , "Group decision support systems" Dec. Support Syst. 3(1987)233-242.
  2. Shih H. S. , Wang C. H. , Lee E. S. , "A multi-attribute GDSS for aiding problem solving" Mathematical and Computer Modeling. 39(2004), 1397-1412.
  3. Chun, K. J. , Park, H. K. ,"Examining the conflicting results of GDSS research" Information & Management, 33 (1998), 313-325.
  4. Chih-Fong Tsai, Yu-Chieh Hsiao "Combining multiple feature selection methods for stock prediction: union, intersection, and multi-intersection approaches" Decision Support Systems, 50 (1) (December 2010), pp. 258–269.
  5. Fan, SC, Shen, QP, Luo, XC , and Xue, XL "A Comparative Study of Traditional and GDSS-Supported Value Management Workshops" Journal of Construction Engineering and Management (2010).
  6. Z. Pawlak, and A. Skorwon "Rudiments of rough sets" Information Sciences, vol. 177, no. 1, pp. 3-27, 2007.
  7. Y. Peng, Y. Zhang, Y. Tang, S. M. Li, "An incident information management framework based on data integration, data mining, and multi-criteria decision making" Decision Support Systems, 51 (2) (2011), pp. 316–327.
  8. R. S?owi?ski, S. Greco, B. Matarazzo "Rough sets in decision making" R. A. Meyers (Ed. ), Encyclopedia of Complexity and Systems Science, Springer, New York (2009), pp. 7753–7786.
  9. Grzymala-Busse J. W. " A new version of the rule induction system LERS" Fundamental Informaticae 1997; 31: 27-39. (mathematical tools to discover patterns hidden in data).
  10. J. B?aszczy?ski, R. S?owi?ski, M. , Szelag "Sequential covering rule induction algorithm for variable consistency rough set approaches" Information Sciences, 181 (2011), pp. 987–1002.
  11. T. Y. Lin and N. Cercone "Rough Sets and Data Mining" Norwell, MA: Kluwer, 2000.
  12. T. -S. Lim, W. -Y. Loh, and Y. -S. Shih, "A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms," Mach. Learn. , vol. 40, pp. 203–228, 2000.
  13. Lara Khansa, Divakaran Liginlal "Predicting stock market returns from malicious attacks: a comparative analysis of vector autoregression and time-delayed" neural networks Decision Support Systems, 51 (4) (2011), pp. 745–759.
  14. C. Blake and C. J. Merz "UCI repository of machine learning databases" [Machine-readable data repository]. Univ. of California, Dept. Information and Computer Science, Irvine, CA. , 2001.
  15. J. W. Grzymala-Busse "MLEM2: A new algorithm for rule induction from imperfect data" Proceedings of the 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Annecy, France, IPMU 2002, July 1-5, 2002, 243-250.
  16. Efraim Turban, Jay E. Aronson, Ting-Peng Liang (2008). "Decision Support Systems and Intelligent Systems". p. 574.
  17. Z. Pawlak, "Rough sets and intelligent data analysis", Information Sciences, vol. 147, pp. 1-12, 2002.
  18. Z. Pawlak, and R. Slowinski, "Rough set approach to multi–attribute decision analysis" European Journal of Operational Research, vol. 72, pp. 443-449, 1994.
  19. The WEKA Homepage, http://www. cs. waikato. ac. nz/~ml , 2008.
  20. The RSES Homepage, http://logic. mimuw. edu. pl/~rses
Index Terms

Computer Science
Information Sciences

Keywords

Group Decision Support Systems Rough set Classification accuracy

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