An enhanced fuzzy linear regression model with more flexible spreads

Lu, Jingli; Wang, Ruili

doi:10.1016/j.fss.2009.02.023

Cited by 49 publications

(21 citation statements)

References 24 publications

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“…This example illustrates the potential deficiency of the fuzzy linear regression that it may not be always true that the estimated uncertainty increases with the increasing objective features. This example shows that fuzzy linear regression may estimate inaccurate uncertainty of aesthetic quality assessment when the certainty is not linearly correlated with the objective features [39].…”

Section: Fuzzy Regression For Aesthetic Quality Estimationmentioning

confidence: 95%

“…The result for the median of the 30 runs is used as the comparison. The proposed NON-SC-FR is compared with the recently-developed fuzzy regression methods namely LW-FR [39], HW-FR [20], GP-FR [4] and U-FR [61], where they are particularly developed to address the nonlinear or varying uncertainty of training samples. LW-FR and HW-FR are developed for both fuzzy and crisp output-data; GP-FR is developed for crisp-output-data; U-FR is developed for fuzzyoutput-data.…”

Section: Evaluations Of Algorithmic Effectivenessmentioning

confidence: 99%

“…Several approaches have been proposed to address this shortcoming of estimating uncertainty [25,46,62]. Analytical study demonstrates that those methods still cannot address decreasing or statics uncertainty trends when magnitudes of objective features are increasing [39].…”

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confidence: 99%

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A Flexible Fuzzy Regression Method for Addressing Nonlinear Uncertainty on Aesthetic Quality Assessments

Chan

Lam

Yiu

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

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-Development of new products or services requires knowledge and understanding of aesthetic qualities that correlate to perceptual pleasure. As it is not practical to develop a survey to assess aesthetic quality for all objective features of a new product or service, it is necessary to develop a model to predict aesthetic qualities. In this paper, a fuzzy regression method is proposed to predict aesthetic quality from a given set of objective features and to account for uncertainty in human assessment. The proposed method overcomes the shortcoming of statistical regression, which can predict only quality magnitudes but cannot predict quality uncertainty. The proposed method also attempts to improve traditional fuzzy regressions, which simulate a single characteristic with which the estimated uncertainty can only increase with the increasing magnitudes of objective features. The proposed fuzzy regression method uses genetic programming (GP) to develop nonlinear structures of the models, and model coefficients are determined by optimizing the fuzzy criteria. Hence the developed model can be used to fit the nonlinearities of sample magnitudes and uncertainties. The effectiveness and the performance of the proposed method are evaluated by the case study of perceptual images, which are involved with different sampling natures and with different amounts of samples. This case study attempts to address different characteristics of human assessments. The outcomes demonstrate that more robust models can be developed by the proposed fuzzy regression method compared with the recently developed fuzzy regression methods, when the model characteristics and fuzzy criteria are taken into account.

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Section: Fuzzy Regression For Aesthetic Quality Estimationmentioning

confidence: 95%

Section: Evaluations Of Algorithmic Effectivenessmentioning

confidence: 99%

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A Flexible Fuzzy Regression Method for Addressing Nonlinear Uncertainty on Aesthetic Quality Assessments

Chan

Lam

Yiu

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

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“…According to the definition of the error, FR models are classified in two categories: probabilistic with LP, and LS approaches. In the former approach, the aim is to minimize the overall fuzziness by minimizing the total spread of the fuzzy coefficients while the estimated outputs and the observed ones are within a certain h-level of confidence [30]. The term h expresses the fitness between the estimated fuzzy outputs and the observed ones.…”

Section: Fuzzy Linear Regression Analysis (Flra)mentioning

confidence: 99%

The prediction of trust rating based on the quality of services using fuzzy linear regression

Mashinchi

Orgun

et al. 2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

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Abstract-With the advent of service-oriented computing, the issue of trust and Quality of Service (QoS) have become increasingly important. In service-oriented environments, when there are a few service providers providing the same service, a service client would be keen to know the trustworthiness of each service provider in the forthcoming transaction. The trust value of a delivered service from a service provider can be predicted according to a set of advertised QoS data collected by the trust management authority. Although trust and QoS are qualitative by nature, most data sets represent trust and QoS in the ordinal form for the sake of simplicity.This paper introduces a new approach based on Fuzzy Linear Regression Analysis (FLRA) to extract qualitative information from quantitative data and so use the obtained qualitative information for better modeling of the data. For verification purposes, the proposed approach can be applied for the trust prediction in the forthcoming transaction based on a set of advertised QoS in service-oriented environments.

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“…Chen and Dang [10] proposed a three-phase method to construct the FR model with variable spreads to resolve the problem of increasing spreads. Lu and Wang [30] proposed an enhanced fuzzy linear regression model (FLR FS ). Shakouri and Nadimi [43] introduced an approach to find the parameters of an FLR with crisp inputs and fuzzy outputs.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy radial basis function network for fuzzy regression with fuzzy input and fuzzy output

Pehlivan

Apaydin

2016

Complex Intell. Syst.

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In this study, fuzzy regression (FR) models with fuzzy inputs and outputs are discussed. Some of the FR methods based on linear programming and fuzzy least squares in the literature are explained. Within this study, we propose a Fuzzy Radial Basis Function (FRBF) Network to obtain the estimations for FR model in the case that inputs and outputs are symmetric/nonsymmetric triangular fuzzy numbers. Proposed FRBF Network approach is a fuzzification of the inputs, outputs and weights of traditional RBF Network and it can be used as an alternative to FR methods. The FRBF Network approach is constructed on the basis of minimizing the square of the total difference between observed and estimated outputs. A simple training algorithm from the cost function of the FRBF Network through Backpropagation algorithm is developed in this study. The advantage of our proposed approach is its simplicity and easy computation as well as its performance. To compare the performance of the proposed method with those given in the literature, three numerical examples are presented.

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An enhanced fuzzy linear regression model with more flexible spreads

Cited by 49 publications

References 24 publications

A Flexible Fuzzy Regression Method for Addressing Nonlinear Uncertainty on Aesthetic Quality Assessments

A Flexible Fuzzy Regression Method for Addressing Nonlinear Uncertainty on Aesthetic Quality Assessments

The prediction of trust rating based on the quality of services using fuzzy linear regression

Fuzzy radial basis function network for fuzzy regression with fuzzy input and fuzzy output

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