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

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.

Comparative Assessment of Multiple Linear Regression and Fuzzy Linear Regression Models

“…Darwish et al [14] presented a regression model for a special case of interval type-2 fuzzy sets based on the least squares estimation technique. Shafi et al [46] proposed a hybrid approach by augmenting FLR with symmetric parameter model and fuzzy c-means method. Pérez-Cañedo et al [40] proposed two new fuzzy goal programming methods based on linear and Chebyshev scalarisations with a view to solve the problem of fully fuzzy multi-objective linear programming.…”