Object-parameter approaches to predicting unknown data in an incomplete fuzzy soft set

• Autorzy: Liu Y. , Qin K. , Rao C. , Alhaji Mahamadu M.

Identyfikatory

DOI

10.1515/amcs-2017-0011

• Języki publikacji: EN

Abstrakty

EN

The research on incomplete fuzzy soft sets is an integral part of the research on fuzzy soft sets and has been initiated recently. In this work, we first point out that an existing approach to predicting unknown data in an incomplete fuzzy soft set suffers from some limitations and then we propose an improved method. The hidden information between both objects and parameters revealed in our approach is more comprehensive. Furthermore, based on the similarity measures of fuzzy sets, a new adjustable object-parameter approach is proposed to predict unknown data in incomplete fuzzy soft sets. Data predicting converts an incomplete fuzzy soft set into a complete one, which makes the fuzzy soft set applicable not only to decision making but also to other areas. The compared results elaborated through rate exchange data sets illustrate that both our improved approach and the new adjustable object-parameter one outperform the existing method with respect to forecasting accuracy.

Słowa kluczowe

EN

fuzzy soft set; incomplete fuzzy soft set; object parameter approach; prediction; similarity measures

PL

zbiór rozmyty; podejście obiektowe; miara podobieństwa

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2017
• Tom: Vol. 27, no. 1
• Strony: 157--167
• Opis fizyczny: Bibliogr. 33 poz., tab.

Twórcy

autor

Liu Y.

• College of Mathematics, Southwest Jiaotong University, Chengdu 610031, Sichuan, PR China

autor

Qin K.

• College of Mathematics, Southwest Jiaotong University, Chengdu 610031, Sichuan, PR China

autor

Rao C.

• College of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, Sichuan, PR China

autor

Alhaji Mahamadu M.

• Council for Scientific and Industrial Research, PO 132, Accra, Ghana

Bibliografia

• [1] Alcantud, J.C.R. (2016). A novel algorithm for fuzzy soft set based decision making from multiobserver input parameter data set, Information Fusion 29: 142–148.
• [2] Atanassov, K.T. (1986). Intuitionistic fuzzy sets, Fuzzy Sets and Systems 20(1): 87–96.
• [3] Deng, T. and Wang, X. (2013). An object-parameter approach to predicting unknown data in incomplete fuzzy soft sets, Applied Mathematical Modelling 37(6): 4139–4146.
• [4] Fan, J. (2002). Some new similarity measures, Journal of Xi’an Institute of Posts and Telecommunications 3(7): 69–71.
• [5] Feng, F., Liu, X., Leoreanu-Fotea, V. and Jun, Y.B. (2011). Soft sets and soft rough sets, Information Sciences 181(6): 1125–1137.
• [6] Gau, W.L. and Buehrer, D.J. (1993). Vague sets, IEEE Transactions on Systems, Man, and Cybernetics 23(2): 610–614.
• [7] Herawan, T. and Deris, M.M. (2011). A soft set approach for association rules mining, Knowledge-Based Systems 24(1): 186–195.
• [8] Jiang, Y., Liu, H., Tang, Y. and Chen, Q. (2011). Semantic decision making using ontology-based soft sets, Mathematical and Computer Modelling 53(5): 1140–1149.
• [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 Applications 60(3): 906–918.
• [10] Jun, Y.B., Lee, K.J. and Park, C.H. (2009). Soft set theory applied to ideals in d-algebras, Computers & Mathematics with Applications 57(3): 367–378.
• [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 Mathematics 236(6): 1521–1530.
• [12] Li, Y., Qin, K. and He, X. (2014). Some new approaches to constructing similarity measures, Fuzzy Sets and Systems 234(1): 46–60.
• [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 Medicine 64: 161–171.
• [14] Li, Z., Xie, N. and Wen, G. (2015b). Soft coverings and their parameter reductions, Applied Soft Computing 31: 48–60.
• [15] Li, Z. and Xie, T. (2014). The relationship among soft sets, soft rough sets and topologies, Soft Computing 18(4): 717–728.
• [16] Maji, P.K., Biswas, R. and Roy, A.R. (2001). Fuzzy soft sets, Journal of Fuzzy Mathematics 9(3): 589–602.
• [17] Molodtsov, D. (1999). Soft set theory—first results, Computers & Mathematics with Applications 37(4): 19–31.
• [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 Computing 41: 148–156.
• [19] Nowicki, R. (2010). On classification with missing data using rough-neuro-fuzzy systems, International Journal of Applied Mathematics and Computer Science 20(1): 55–67, DOI: 10.2478/v10006-010-0004-8.
• [20] Pawlak, Z. (1982). Rough sets, International Journal of Computer & Information Sciences 11(5): 341–356.
• [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 Science 22(4): 817–828, DOI: 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 Systems 36: 139–145.
• [23] Roy, A.R. and Maji, P. (2007). A fuzzy soft set theoretic approach to decision making problems, Journal of Computational and Applied Mathematics 203(2): 412–418.
• [24] Siwek, K. and Osowski, S. (2016). Data mining methods for prediction of air pollution, International Journal of Applied Mathematics and Computer Science 26(2): 467–478, DOI: 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 Mathematics 228(1): 326–333.
• [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 Systems 8(5): 959–976.
• [28] Xu, W., Ma, J., Wang, S. and Hao, G. (2010). Vague soft sets and their properties, Computers & Mathematics with Applications 59(2): 787–794.
• [29] Yang, X., Lin, T.Y., Yang, J., Li, Y. and Yu, D. (2009). Combination of interval-valued fuzzy set and soft set, Computers & Mathematics with Applications 58(3): 521–527.
• [30] Zadeh, L.A. (1965). Fuzzy sets, Information and Control 8(3): 338–353.
• [31] Zhao, A. and Guan, H. (2015). Fuzzy-valued linguistic soft set theory and multi-attribute decision-making application, Chaos, Solitons & Fractals 89: 2–7.
• [32] Zhong, N. and Skowron, A. (2001). A rough set-based knowledge discovery process, International Journal of Applied Mathematics and Computer Science 11(3): 603–619.
• [33] Zou, Y. and Xiao, Z. (2008). Data analysis approaches of soft sets under incomplete information, Knowledge-Based Systems 21(8): 941–

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).