The Experts below are selected from a list of 264 Experts worldwide ranked by ideXlab platform
Tahereh Razzaghnia - One of the best experts on this subject based on the ideXlab platform.
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Fuzzy nonparametric regression based on an adaptive neuro-fuzzy inference system
Neurocomputing, 2016Co-Authors: Sedigheh Danesh, Rahman Farnoosh, Tahereh RazzaghniaAbstract:In this paper, a system, namely the adaptive neuro-fuzzy inference system (ANFIS), is investigated and used for fuzzy nonparametric regression function prediction with Crisp Input and fuzzy output. The fuzzy least squares problem based on Diamond[U+05F3]s distance is proposed to optimize the consequent parameters in the hybrid algorithm of the adaptive neuro-fuzzy inference system method. Also, an algorithm is proposed to reduce bias and the boundary effect of the estimates of the underlying regression function. Various examples are used to illustrate and test the performance of this approach. The proposed method is compared with the local linear smoothing method for investigating the accuracy of the approach. The results demonstrate that the proposed method not only gives less biased estimates of the center line, the lower and the upper limit lines of underlying fuzzy regression function but also reduces bias and the boundary effect of the estimates of the underlying regression function by using the proposed algorithm significantly.
Kyung Ha Seok - One of the best experts on this subject based on the ideXlab platform.
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hybrid fuzzy least squares support vector machine regression for Crisp Input and fuzzy output
Communications for Statistical Applications and Methods, 2010Co-Authors: Jooyong Shim, Kyung Ha Seok, Changha HwangAbstract:Hybrid fuzzy regression analysis is used for integrating randomness and fuzziness into a regression model. Least squares support vector machine(LS-SVM) has been very successful in pattern recognition and function estimation problems for Crisp data. This paper proposes a new method to evaluate hybrid fuzzy linear and nonlinear regression models with Crisp Inputs and fuzzy output using weighted fuzzy arithmetic(WFA) and LS-SVM. LS-SVM allows us to perform fuzzy nonlinear regression analysis by constructing a fuzzy linear regression function in a high dimensional feature space. The proposed method is not computationally expensive since its solution is obtained from a simple linear equation system. In particular, this method is a very attractive approach to modeling nonlinear data, and is nonparametric method in the sense that we do not have to assume the underlying model function for fuzzy nonlinear regression model with Crisp Inputs and fuzzy output. Experimental results are then presented which indicate the performance of this method.
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support vector interval regression machine for Crisp Input and output data
Fuzzy Sets and Systems, 2006Co-Authors: Changha Hwang, Dug Hun Hong, Kyung Ha SeokAbstract:Support vector regression (SVR) has been very successful in function estimation problems for Crisp data. In this paper, we propose a robust method to evaluate interval regression models for Crisp Input and output data combining the possibility estimation formulation integrating the property of central tendency with the principle of standard SVR. The proposed method is robust in the sense that outliers do not affect the resulting interval regression. Furthermore, the proposed method is model-free method, since we do not have to assume the underlying model function for interval nonlinear regression model with Crisp Input and output. In particular, this method performs better and is conceptually simpler than support vector interval regression networks (SVIRNs) which utilize two radial basis function networks to identify the upper and lower sides of data interval. Five examples are provided to show the validity and applicability of the proposed method.
Changha Hwang - One of the best experts on this subject based on the ideXlab platform.
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hybrid fuzzy least squares support vector machine regression for Crisp Input and fuzzy output
Communications for Statistical Applications and Methods, 2010Co-Authors: Jooyong Shim, Kyung Ha Seok, Changha HwangAbstract:Hybrid fuzzy regression analysis is used for integrating randomness and fuzziness into a regression model. Least squares support vector machine(LS-SVM) has been very successful in pattern recognition and function estimation problems for Crisp data. This paper proposes a new method to evaluate hybrid fuzzy linear and nonlinear regression models with Crisp Inputs and fuzzy output using weighted fuzzy arithmetic(WFA) and LS-SVM. LS-SVM allows us to perform fuzzy nonlinear regression analysis by constructing a fuzzy linear regression function in a high dimensional feature space. The proposed method is not computationally expensive since its solution is obtained from a simple linear equation system. In particular, this method is a very attractive approach to modeling nonlinear data, and is nonparametric method in the sense that we do not have to assume the underlying model function for fuzzy nonlinear regression model with Crisp Inputs and fuzzy output. Experimental results are then presented which indicate the performance of this method.
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support vector interval regression machine for Crisp Input and output data
Fuzzy Sets and Systems, 2006Co-Authors: Changha Hwang, Dug Hun Hong, Kyung Ha SeokAbstract:Support vector regression (SVR) has been very successful in function estimation problems for Crisp data. In this paper, we propose a robust method to evaluate interval regression models for Crisp Input and output data combining the possibility estimation formulation integrating the property of central tendency with the principle of standard SVR. The proposed method is robust in the sense that outliers do not affect the resulting interval regression. Furthermore, the proposed method is model-free method, since we do not have to assume the underlying model function for interval nonlinear regression model with Crisp Input and output. In particular, this method performs better and is conceptually simpler than support vector interval regression networks (SVIRNs) which utilize two radial basis function networks to identify the upper and lower sides of data interval. Five examples are provided to show the validity and applicability of the proposed method.
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quadratic loss support vector interval regression machine for Crisp Input output data
Journal of the Korean Data and Information Science Society, 2004Co-Authors: Changha HwangAbstract:Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for Crisp data. This paper proposes a new method to evaluate interval regression models for Crisp Input-output data. The proposed method is based on quadratic loss SVM, which implements quadratic programming approach giving more diverse spread coefficients than a linear programming one. The proposed algorithm here is model-free method in the sense that we do not have to assume the underlying model function. Experimental result is then presented which indicate the performance of this algorithm.
Antonio Calcagni - One of the best experts on this subject based on the ideXlab platform.
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a generalized maximum entropy gme approach for Crisp Input fuzzy output regression model
Quality & Quantity, 2014Co-Authors: Enrico Ciavolino, Antonio CalcagniAbstract:In this paper we present a Crisp-Input/fuzzy-output regression model based on the rationale of generalized maximum entropy (GME) method. The approach can be used in several situations in which one have to handle with particular problems, such as small samples, ill-posed design matrix (e.g., due to the multicollinearity), estimation problems with inequality constraints, etc. After having described the GME-fuzzy regression model, we consider an economic case study in which the features provided from GME approach are evaluated. Moreover, we also perform a sensitivity analysis on the main results of the case study in order to better evaluate some features of the model. Finally, some critical points are discussed together with suggestions for further works. Copyright Springer Science+Business Media Dordrecht 2014
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A generalized maximum entropy (GME) approach for Crisp-Input/fuzzy-output regression model
Quality & Quantity, 2013Co-Authors: Enrico Ciavolino, Antonio CalcagniAbstract:In this paper we present a Crisp-Input/fuzzy-output regression model based on the rationale of generalized maximum entropy (GME) method. The approach can be used in several situations in which one have to handle with particular problems, such as small samples, ill-posed design matrix (e.g., due to the multicollinearity), estimation problems with inequality constraints, etc. After having described the GME-fuzzy regression model, we consider an economic case study in which the features provided from GME approach are evaluated. Moreover, we also perform a sensitivity analysis on the main results of the case study in order to better evaluate some features of the model. Finally, some critical points are discussed together with suggestions for further works. Copyright Springer Science+Business Media Dordrecht 2014
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a generalized maximum entropy gme approach to Crisp Input fuzzy output regression model
Advances in Latent Variables - Methods Models and Applications, 2013Co-Authors: Antonio CalcagniAbstract:In this short-paper we describe the application of Generalized Maximum Entropy Method of Estimation on Crisp-Input/fuzzy-output regression model. In order to highlight some interesting features provided by this method, we carried out a Monte Carlo experiment in which both models were tested by varying multicollinearity in the design matrix and by considering two levels of sample sizes. Next, the performances of the two methods were evaluate in terms of standard errors of the regression coefficient and RMSEs for the fuzzy dependent variables.
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A Generalized Maximum Entropy (GME) approach to Crisp-Input/fuzzy-output regression model
Advances in Latent Variables - Methods Models and Applications, 2013Co-Authors: Antonio CalcagniAbstract:In this short-paper we describe the application of Generalized Maximum Entropy Method of Estimation on Crisp-Input/fuzzy-output regression model. In order to highlight some interesting features provided by this method, we carried out a Monte Carlo experiment in which both models were tested by varying multicollinearity in the design matrix and by considering two levels of sample sizes. Next, the performances of the two methods were evaluate in terms of standard errors of the regression coefficient and RMSEs for the fuzzy dependent variables.
Sedigheh Danesh - One of the best experts on this subject based on the ideXlab platform.
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Fuzzy nonparametric regression based on an adaptive neuro-fuzzy inference system
Neurocomputing, 2016Co-Authors: Sedigheh Danesh, Rahman Farnoosh, Tahereh RazzaghniaAbstract:In this paper, a system, namely the adaptive neuro-fuzzy inference system (ANFIS), is investigated and used for fuzzy nonparametric regression function prediction with Crisp Input and fuzzy output. The fuzzy least squares problem based on Diamond[U+05F3]s distance is proposed to optimize the consequent parameters in the hybrid algorithm of the adaptive neuro-fuzzy inference system method. Also, an algorithm is proposed to reduce bias and the boundary effect of the estimates of the underlying regression function. Various examples are used to illustrate and test the performance of this approach. The proposed method is compared with the local linear smoothing method for investigating the accuracy of the approach. The results demonstrate that the proposed method not only gives less biased estimates of the center line, the lower and the upper limit lines of underlying fuzzy regression function but also reduces bias and the boundary effect of the estimates of the underlying regression function by using the proposed algorithm significantly.
