Robust Fuzzy Varying Coefficient Regression Analysis with Crisp Inputs and Gaussian Fuzzy Output

This study presents a fuzzy varying coefficient regression model after deleting the outliers to improve the feasibility and effectiveness of the fuzzy regression model. The objective of our methodology is to allow the fuzzy regression coefficients to vary with a covariate, and simultaneously avoid the impact of data contaminated by outliers. In this paper, fuzzy regression coefficients are represented by Gaussian fuzzy numbers. We also formulate suitable goodness of fit to evaluate the performance of the proposed methodology. An example is given to demonstrate the effectiveness of our methodology. Category: Smart and intelligent computing

[1]  Reshma Khemchandani,et al.  Regularized least squares fuzzy support vector regression for financial time series forecasting , 2009, Expert Syst. Appl..

[2]  J. Watada,et al.  Fuzzy robust regression analysis , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[3]  Dug Hun Hong,et al.  Support vector fuzzy regression machines , 2003, Fuzzy Sets Syst..

[4]  Pierpaolo D'Urso,et al.  Goodness of fit and variable selection in the fuzzy multiple linear regression , 2006, Fuzzy Sets Syst..

[5]  Jian-Ling Cui,et al.  A fuzzy varying coefficient model and its estimation , 2010, Comput. Math. Appl..

[6]  H. Moskowitz,et al.  Fuzzy versus statistical linear regression , 1996 .

[7]  James J. Buckley,et al.  Monte Carlo methods in fuzzy linear regression II , 2007, Soft Comput..

[8]  Kit Yan Chan,et al.  Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers , 2010, Inf. Sci..

[9]  Ping-Feng Pai,et al.  A fuzzy support vector regression model for business cycle predictions , 2010, Expert Syst. Appl..

[10]  Richard Y. K. Fung,et al.  Fuzzy regression-based mathematical programming model for quality function deployment , 2004 .

[11]  Miin-Shen Yang,et al.  An omission approach for detecting outliers in fuzzy regression models , 2006, Fuzzy Sets Syst..

[12]  Rob Law,et al.  Fuzzy support vector regression machine with penalizing Gaussian noises on triangular fuzzy number space , 2010, Expert Syst. Appl..

[13]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[14]  S. M. Taheri,et al.  FUZZY LINEAR REGRESSION BASED ON LEAST ABSOLUTES DEVIATIONS , 2012 .

[15]  Jung-Hsien Chiang,et al.  Fuzzy Regression Analysis by Support Vector Learning Approach , 2008, IEEE Transactions on Fuzzy Systems.

[16]  Frederick R. Forst,et al.  On robust estimation of the location parameter , 1980 .

[17]  Ana Colubi,et al.  A linear regression model for imprecise response , 2010, Int. J. Approx. Reason..

[18]  J. Watada,et al.  Possibilistic linear systems and their application to the linear regression model , 1988 .

[19]  Michael S. Waterman,et al.  A Restricted Least Squares Problem , 1974 .

[20]  Pierpaolo D'Urso,et al.  Robust fuzzy regression analysis , 2011, Inf. Sci..

[21]  Hsien-Chung Wu,et al.  The construction of fuzzy least squares estimators in fuzzy linear regression models , 2011, Expert Syst. Appl..

[22]  H. Tanka.  Fuzzy data analysis by possibilistic linear models , 1987 .

[23]  Phil Diamond,et al.  Fuzzy least squares , 1988, Inf. Sci..

[24]  Yaning Liu,et al.  Anomaly Detection in Medical Wireless Sensor Networks , 2013, J. Comput. Sci. Eng..

[25]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[26]  Wei Zhang,et al.  Counter-Based Approaches for Efficient WCET Analysis of Multicore Processors with Shared Caches , 2013, J. Comput. Sci. Eng..

[27]  James J. Buckley,et al.  Monte Carlo methods in fuzzy linear regression , 2007, Soft Comput..