Improving Group Decision Support Systems using Rough Set

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.

[1]  Efraim Turban,et al.  Decision Support Systems and Intelligent Systems (7th Edition) , 2004 .

[2]  Paul Gray,et al.  Group decision support systems , 1987, Decis. Support Syst..

[3]  Chih-Hung Wang,et al.  A multiattribute GDSS for aiding problem-solving , 2004 .

[4]  Jerzy W. Grzymala-Busse,et al.  A New Version of the Rule Induction System LERS , 1997, Fundam. Informaticae.

[5]  Hung Kook Park,et al.  Examining the conflicting results of GDSS research , 1998, Inf. Manag..

[6]  Efraim Turban,et al.  Decision support systems and intelligent systems , 1997 .

[7]  Zdzislaw Pawlak,et al.  Rough sets and intelligent data analysis , 2002, Inf. Sci..

[8]  Z. Pawlak,et al.  Rough set approach to multi-attribute decision analysis , 1994 .

[9]  Salvatore Greco,et al.  Rough Sets in Decision Making , 2009, Encyclopedia of Complexity and Systems Science.

[10]  Roman Słowiński,et al.  Sequential covering rule induction algorithm for variable consistency rough set approaches , 2011, Inf. Sci..

[11]  Z. Pawlak Rough Sets and Data Mining , 2022 .

[12]  Yong Zhang,et al.  An incident information management framework based on data integration, data mining, and multi-criteria decision making , 2011, Decis. Support Syst..

[13]  Draft ) A Comparative Study of Traditional and GDSS-Supported Value Management Workshops , 2009 .

[14]  Qiping Shen,et al.  Comparative Study of Idea Generation between Traditional Value Management Workshops and GDSS-Supported Workshops , 2007 .

[15]  Chih-Fong Tsai,et al.  Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches , 2010, Decis. Support Syst..

[16]  Andrzej Skowron,et al.  Rudiments of rough sets , 2007, Inf. Sci..

[17]  Wei-Yin Loh,et al.  A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms , 2000, Machine Learning.

[18]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[19]  Lara Khansa,et al.  Predicting stock market returns from malicious attacks: A comparative analysis of vector autoregression and time-delayed neural networks , 2011, Decis. Support Syst..