Abstract
Soft set time complexity is become really a problem when the numbers of parameters are increased. In order to solve time complexity problem, it necessary to reduce the boundary of optimal soft set growth and due to this the time cost can be enhanced. Several soft set methods are determining the soft set reduction but in performing the reduction it spends more time to produce the result and this happens because the false candidate sets are a part of solution. So, if the boundary of candidate reduction is narrowed then the reduction process will speed up. In this paper, we proposed a new method which reducing the boundary of candidate reduction using Lipschitz constant and wavelet discrete transform to eliminate large false sets from the solution. In Lipschitz constant function the value of candidate implies are determined, where based on wavelet WDT the false sets which is not in the form of implies also can be determined. The proposed method remove an inconsistency noise from the soft set in a pre-processing filtering based on if then method which help to classify further reduction in short time. It found that by using Lipschitz constant function and wavelet discrete transform the reduction time can be enhanced several times comparing to previous reduction methods. The result indicates that Lipschitz constant function and wavelet WDT algorithm. It complements each other to determine candidate soft set reduction.
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References
Molodtsov, D.: Soft set theory-first results. Comput. Math. Appl. 37, 19–31 (1999)
Chen, D., Tsang, E.C.C., Yeung, D.S., Wang, X.: The parameterization reduction of soft sets and its applications. Comput. Math. Appl. 49, 757–763 (2005)
Kong, Z., Gao, L., Wang, L., Li, S.: The normal parameter reduction of soft sets and its algorithm. Comput. Math. Appl. 56, 3029–3037 (2008)
Zhao, Y., Luo, F., Wong, S.K.M., Yao, Y.Y.: A general definition of an attribute reduct. In: Lecture Notes in Artificial Intelligent, pp. 101–108 (2007)
Rose, A.N.M., Awang, M.I., Hassan, H., Zakaria, A.H., Herawan, T., Deris, M.M.: Hybrid Reduction in Soft Set Decision Making. Springer-ICIC, pp. 108–115 (2011)
Rose, A.N.M., Herawan, T., Deris, M.M.: A framework of decision making based on maximal supported sets. In: ISNN 2010, Part I. LNCS, vol. 6063, pp. 473–482. Springer, Berlin (2010)
Mohammed, M.A.T., Mohd, W.M.B.W., Arshah, R.B.A., Yao, L.: Parameter reduction comparisons. Asian Acad. Res. Assoc. AARJSH 1(19) (2014)
Maji, P.K., Roy, A.R., Biswas, R.: An application of soft sets in a decision making problem. Comput. Math. Appl. 44, 1077–1083 (2002)
Kumar, D.A., Rengasamy, R.: Parameterization reduction using soft set theory for better decision making. In: Proceedings of the 2013 International Conference on Pattern Recognition, 21–22 February. IEEE, India (2013)
Maa, X., Sulaiman, N., Qin, H., Herawana, T., Zain, J.M.: A new efficient normal parameter reduction algorithm of soft sets. Comput. Math. Appl. 62, 588–598 (2011)
Gómez, D., Tinguaro, R.J., Montero, J., Bustince, H., Barrenechea, E.: n-Dimensional overlap functions. Fuzzy Sets Syst. 6702 (2014)
Ibrir, S., Bettayeb, M.: Model reduction of a class of discrete-time nonlinear systems. Appl. Math. Comput. 250(2015), 78–93 (2014)
Edalat, A., Lieutier, A., Pattinson, D.: Computational Model for Multi-Variable Differential Calculus
Hamad, M.M.: Data mining and statistical methods used for scanning categorical data. Comput. Math. Appl. J. Al-Anbar Univ. Pure Sci. 1(2) (2007). ISSN: 1991-8941
Sifuzzaman, M., Islam, M.R., Ali, M.Z.: Application of wavelet transform and its advantages compared to Fourier transform. J. Phys. Sci. 13(2009), 121–134 (2014)
Aghabozorgi, S., Wah, T.Y., Herawan, T., Jalab, H.A., Shaygan, M.A., Jalali, A.: A hybrid algorithm for clustering of time series data based on affinity search technique. Hindawi Publishing Corporation Sci. World J. 2014, Article ID 562194, 12p (2014)
Hakim, R.B.F., Sari, E.N., Herawan, T.: On if-then multi soft sets-based decision making. In: Linawati, M.M.S., et al. (eds.) ICT-EurAsia 2014. LNCS, vol. 8407, pp. 306–315 (2014)
Mohammed, M.A.T, Mohd, W.M.W, Arshah, R.A., Mungad, M., Sutoyo, E., Chiroma, H.: Hybrid Filter for Attributes Reduction in Soft Set. DaEng (2015)
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Mohammed, M.A.T., Mohd, W.M.W., Arshah, R.A., Mungad, M., Sutoyo, E., Chiroma, H. (2019). A New Support Value Method Filtering Based on Object Support Partition for Soft Reduction. In: Abawajy, J., Othman, M., Ghazali, R., Deris, M., Mahdin, H., Herawan, T. (eds) Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015) . Lecture Notes in Electrical Engineering, vol 520. Springer, Singapore. https://doi.org/10.1007/978-981-13-1799-6_28
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DOI: https://doi.org/10.1007/978-981-13-1799-6_28
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