Prediction in a Hybrid of Fuzzy Linear Regression with Symmetric Parameter Model and Fuzzy C-Means Method Using Simulation Data

  • Authors

    • Muhammad Ammar Shafi
    • Mohd Saifullah Rusiman
    • Kavikumar Jacob
    • Nor Shamsidah Amir Hamzah
    • Norziha Che Him
    • Nazeera Mohamad
    2018-11-30
    https://doi.org/10.14419/ijet.v7i4.30.22351
  • Fuzzy C-means, Fuzzy linear regression, Hybrid Model.

  • Abstract

    The objective of fuzzy linear regression model (FLRM) to predict the dependent variable and independent variables in vague phenomenon. In this study, several models such as fuzzy linear regression model (FLRM), fuzzy linear regression with symmetric parameter (FLWSP) and a hybrid model have been applied to be evaluated by 1000 rows in 1 simulation data. Moreover, the hybrid method was applied between fuzzy linear regression with symmetric parameter (FLRWSP) and fuzzy c-mean (FCM) method to get the effective prediction in a new model and best result in this study. To improve the accuracy of evaluating and predicting, this study employ two measurement error of cross validation statistical technique which are mean square error (MSE) and root mean square error (RMSE). The simulation result suggests that comparison among models using two measurement errors should be to determine the best results. Finally, this study notes that the new hybrid model of FLRWSP and FCM is verified to be a good model with the least value of MSE and RMSE measurement errors.

  • References

    1. [1] Shafi MA, Rusiman MS (2015), The use of fuzzy linear regression models for tumor size in colorectal cancer in hospital of Malaysia. Applied Mathematical Sciences 9, 2749-2759.

      [2] Shafi MA, Rusiman MS, Nor ME, Khamis A, Abdullah SNS, Azmi MS, Abidin MSZ, Ali M (2018), The factors that influence job satisfaction among royal Malaysian customs department employee. Journal of Physics: Conference Series 995, 12-16.

      [3] Shafi MA, Rusiman MS, Hamzah NSA, Nor ME, Azmi NAHM, Latip MFA, Azman AH (2018), The analysis of morphometric data on rocky mountain wolves and artic wolves using statistical method. Journal of Physics: Conference Series 995, 12-15.

      [4] Michael HK, Christopher JN, John N, William Li (2004), Applied linear statistical models. Fifth edition MC GRAW HILL.

      [5] Tanaka H, Uejima S & Asai K (1982), Linear regression analysis with fuzzy model. IEEE Transactions On Systems, Man and Cybernetics 12, 903-907.

      [6] Tanaka H (1987), Fuzzy data analysis by possibilities linear models. Fuzzy Set Syst 24, 363-375.

      [7] George JK, Bo Y (2008), Fuzzy sets and fuzzy logic. Prentice hall of india, new delhi, 110001.

      [8] Zadeh LA (1965), Fuzzy Sets. Inform Control 8, 338-358.

      [9] Zadeh LA (1975). The Concept Of A Linguistic Variable And Its Application To Approximate Reasoning. Information Sciences, Part I : No. 8, pp. 199-249 ; Part II : No. 8, pp. 301-357 ; Part III : No. 9, pp. 43-80

      [10] Bin MAS, Bin MSR, Yusof NSHC (2014), Determinants status of patient after receiving treatment at intensive care unit: a case study in Johor Bahru. Computer, Communications, and Control Technology (I4CT), 2014 International Conference on, 80 – 82.

      [11] Shafi MA, Rusiman MS, Hamzah NSA, Sufahani SF, Khamis A, Azmi NAHM (2018), Fuzzification of quantitative data to predict tumour size of colorectal cancer. Far East Journal of Mathematical Sciences 103, 951-959.

      [12] Shafi MA, Rusiman MS, Razali M, Asyikin SN, Mohamad M (2015), The effective model of linear regressions for colorectal cancer stages in general hospital: a case study in Kuala Lumpur. Australian Journal of Basic and Applied Sciences 9, 7-10.

      [13] Shafi MA, Rusiman MS, Hamzah NSA, Nasibov EN, Azmi NAHM (2016), The use of linear statistical model to predict tumour size of colorectal cancer. Journal of Science and Technology 8.

      [14] Sadat ZZ, Mohebbeat M & Marzieh N (2014). Application of Fuzzy Linear Regression Method for Sensory Evaluation Of Fried Donut. Applied Soft Computing 22, 417-423.

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  • How to Cite

    Shafi, M. A., Rusiman, M. S., Jacob, K., Hamzah, N. S. A., Him, N. C., & Mohamad, N. (2018). Prediction in a Hybrid of Fuzzy Linear Regression with Symmetric Parameter Model and Fuzzy C-Means Method Using Simulation Data. International Journal of Engineering & Technology, 7(4.30), 419-422. https://doi.org/10.14419/ijet.v7i4.30.22351

    Received date: 2018-11-29

    Accepted date: 2018-11-29

    Published date: 2018-11-30