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Object–Parameter Approaches to Predicting Unknown Data in an Incomplete Fuzzy Soft Set Cover

Object–Parameter Approaches to Predicting Unknown Data in an Incomplete Fuzzy Soft Set

Open Access
|May 2017

References

  1. Alcantud, J.C.R. (2016). A novel algorithm for fuzzy soft set based decision making from multiobserver input parameter data set, Information Fusion29: 142–148.10.1016/j.inffus.2015.08.007
  2. Atanassov, K.T. (1986). Intuitionistic fuzzy sets, Fuzzy Sets and Systems20(1): 87–96.10.1016/S0165-0114(86)80034-3
  3. Deng, T. and Wang, X. (2013). An object-parameter approach to predicting unknown data in incomplete fuzzy soft sets, Applied Mathematical Modelling37(6): 4139–4146.10.1016/j.apm.2012.09.010
  4. Fan, J. (2002). Some new similarity measures, Journal of Xi’an Institute of Posts and Telecommunications3(7): 69–71.
  5. Feng, F., Liu, X., Leoreanu-Fotea, V. and Jun, Y.B. (2011). Soft sets and soft rough sets, Information Sciences181(6): 1125–1137.10.1016/j.ins.2010.11.004
  6. Gau, W.L. and Buehrer, D.J. (1993). Vague sets, IEEE Transactions on Systems, Man, and Cybernetics23(2): 610–614.10.1109/21.229476
  7. Herawan, T. and Deris, M.M. (2011). A soft set approach for association rules mining, Knowledge-Based Systems24(1): 186–195.10.1016/j.knosys.2010.08.005
  8. Jiang, Y., Liu, H., Tang, Y. and Chen, Q. (2011). Semantic decision making using ontology-based soft sets, Mathematical and Computer Modelling53(5): 1140–1149.10.1016/j.mcm.2010.11.080
  9. Jiang, Y., Tang, Y., Chen, Q., Liu, H. and Tang, J. (2010). Interval-valued intuitionistic fuzzy soft sets and their properties, Computers & Mathematics with Applications60(3): 906–918.10.1016/j.camwa.2010.05.036
  10. Jun, Y.B., Lee, K.J. and Park, C.H. (2009). Soft set theory applied to ideals in d-algebras, Computers & Mathematics with Applications57(3): 367–378.10.1016/j.camwa.2008.11.002
  11. Kong, Z., Wang, L. and Wu, Z. (2011). Application of fuzzy soft set in decision making problems based on grey theory, Journal of Computational and Applied Mathematics236(6): 1521–1530.10.1016/j.cam.2011.09.016
  12. Li, Y., Qin, K. and He, X. (2014). Some new approaches to constructing similarity measures, Fuzzy Sets and Systems234(1): 46–60.10.1016/j.fss.2013.03.008
  13. Li, Z., Wen, G. and Xie, N. (2015a). An approach to fuzzy soft sets in decision making based on grey relational analysis and Dempster–Shafer theory of evidence: An application in medical diagnosis, Artificial Intelligence in Medicine64: 161–171.10.1016/j.artmed.2015.05.002
  14. Li, Z., Xie, N. and Wen, G. (2015b). Soft coverings and their parameter reductions, Applied Soft Computing31: 48–60.10.1016/j.asoc.2015.02.027
  15. Li, Z. and Xie, T. (2014). The relationship among soft sets, soft rough sets and topologies, Soft Computing18(4): 717–728.10.1007/s00500-013-1108-5
  16. Maji, P.K., Biswas, R. and Roy, A.R. (2001). Fuzzy soft sets, Journal of Fuzzy Mathematics9(3): 589–602.
  17. Molodtsov, D. (1999). Soft set theory—first results, Computers & Mathematics with Applications37(4): 19–31.10.1016/S0898-1221(99)00056-5
  18. Muthukumar, P. and Krishnan, G.S.S. (2016). A similarity measure of intuitionistic fuzzy soft sets and its application in medical diagnosis, Applied Soft Computing41: 148–156.10.1016/j.asoc.2015.12.002
  19. Nowicki, R. (2010). On classification with missing data using rough-neuro-fuzzy systems, International Journal of Applied Mathematics and Computer Science20(1): 55–67, DOI: 10.2478/v10006-010-0004-8.10.2478/v10006-010-0004-8
  20. Pawlak, Z. (1982). Rough sets, International Journal of Computer & Information Sciences11(5): 341–356.10.1007/BF01001956
  21. Qin, H., Ma, X., Herawan, T. and Zain, J.M. (2012a). DFIS: A novel data filling approach for an incomplete soft set, International Journal of Applied Mathematics and Computer Science22(4): 817–828, DOI: 10.2478/v10006-012-0060-3.10.2478/v10006-012-0060-3
  22. Qin, H., Ma, X., Zain, J.M. and Herawan, T. (2012b). A novel soft set approach in selecting clustering attribute, Knowledge-Based Systems36: 139–145.10.1016/j.knosys.2012.06.001
  23. Roy, A.R. and Maji, P. (2007). A fuzzy soft set theoretic approach to decision making problems, Journal of Computational and Applied Mathematics203(2): 412–418.10.1016/j.cam.2006.04.008
  24. Siwek, K. and Osowski, S. (2016). Data mining methods for prediction of air pollution, International Journal of Applied Mathematics and Computer Science26(2): 467–478, DOI: 10.1515/amcs-2016-0033.10.1515/amcs-2016-0033
  25. Wang, P. (1983). Fuzzy Sets and Its Applications, Shanghai Science and Technology Press, Shanghai.
  26. Xiao, Z., Gong, K. and Zou, Y. (2009). A combined forecasting approach based on fuzzy soft sets, Journal of Computational and Applied Mathematics228(1): 326–333.10.1016/j.cam.2008.09.033
  27. Xie, N., Han, Y. and Li, Z. (2015). A novel approach to fuzzy soft sets in decision making based on grey relational analysis and mycin certainty factor, International Journal of Computational Intelligence Systems8(5): 959–976.10.1080/18756891.2015.1099903
  28. Xu, W., Ma, J., Wang, S. and Hao, G. (2010). Vague soft sets and their properties, Computers & Mathematics with Applications59(2): 787–794.10.1016/j.camwa.2009.10.015
DOI: https://doi.org/10.1515/amcs-2017-0011 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 157 - 167
Submitted on: Apr 22, 2016
Accepted on: Oct 15, 2016
Published on: May 4, 2017
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2017 Yaya Liu, Keyun Qin, Chang Rao, Mahamuda Alhaji Mahamadu, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.