A decision making model using soft set and rough set on fuzzy approximation spaces

被引:14
作者
机构
[1] School of Information Technology and Engineering, VIT University, Vellore
[2] School of Computer Science and Engineering, VIT University, Vellore
来源
Acharjya, D.P. (dpacharjya@gmail.com) | 1600年 / Inderscience Enterprises Ltd., 29, route de Pre-Bois, Case Postale 856, CH-1215 Geneva 15, CH-1215, Switzerland卷 / 13期
关键词
Almost indiscernibility; Fuzzy tolerance relation; Intelligent systems; Ordering rules; Rough set; Soft set;
D O I
10.1504/IJISTA.2014.065172
中图分类号
学科分类号
摘要
In modern era of computing, there is a need of development in data analysis and decision making. Most of our tools are crisp, deterministic and precise in character. But general real life situations contains uncertainties. To handle such uncertainties many theories are developed such as fuzzy set, rough set, rough set on fuzzy approximation spaces etc. But all these theories have their own limitations. To overcome the limitations, the concept of soft set is introduced. But, soft set also fails if the attributes in the information system are almost identical rather exactly identical. In this paper, we propose a decision making model that consists of two processes such as preprocess and postprocess to mine decisions. In preprocess we use rough set on fuzzy approximation spaces to get the almost equivalence classes whereas in postprocess we use soft set techniques to obtain decisions. The proposed model is tested over an institutional dataset and the results show practical viability of the proposed research. Copyright © 2014 Inderscience Enterprises Ltd.
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收藏
页码:170 / 186
页数:16
相关论文
共 21 条
  • [1] Acharjya D.P., Rough computing based information retrieval in knowledge discovery databases, Information and Knowledge Management-Tools, Techniques and Practices, pp. 123-153, (2013)
  • [2] Acharjya D.P., Bhattacharjee D., A rough computing based performance evaluation approach for educational institutions, International Journal of Software Engineering and Its Applications, 7, 4, pp. 331-348, (2013)
  • [3] Acharjya D.P., Ezhilarsi L., A knowledge mining model for ranking institutions using rough computing with ordering rules and formal concept analysis, International Journal of Computer Science Issues, 8, 2, pp. 417-425, (2011)
  • [4] Acharjya D.P., Debasrita R., Rahaman M.A., Prediction of missing associations using rough computing and Bayesian classification, International Journal of Intelligent Systems and Applications, 4, 11, pp. 1-13, (2012)
  • [5] Acharjya D.P., Tripathy B.K., Rough sets on fuzzy approximation spaces and applications to distributed knowledge systems, International Journal of Artificial Intelligence and Soft Computing, 1, 1, pp. 1-14, (2008)
  • [6] Acharjya D.P., Tripathy B.K., Rough sets on intuitionistic fuzzy approximation spaces and knowledge representation, International Journal of Artificial Intelligence and Computational Research, 1, 1, pp. 29-36, (2009)
  • [7] Atanasov K.T., Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20, pp. 87-96, (1986)
  • [8] Dubois D., Prade H., Rough fuzzy sets and fuzzy rough sets, International Journal of General System, 17, pp. 191-208, (1990)
  • [9] Lin T.Y., A set theory for soft computing, a unified view of fuzzy sets via neighbourhoods, IEEE International Conference on Fuzzy Systems, pp. 1140-1146, (1996)
  • [10] Maji P.K., Roy A.R., An application of soft sets in a decisiom making problem, Computers and Mathematics with Applications, 44, pp. 1077-1083, (2002)