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Li, J.H., Zeng, W.Y. and Xie, J.J. (2016) A New Fuzzy Regression Model Based on Least Absolute Deviation. Engineering Applications of Artificial Intelligence, 52, 54-64. https://doi.org/10.1016/j.engappai.2016.02.009

has been cited by the following article:

• TITLE: Probabilistic Fuzzy Regression Approach from the Point of View Risk

  AUTHORS: Nana Gao, Qiujun Lu

  KEYWORDS: Probabilistic Fuzzy Regression, Chaos Optimization Algorithm, Risk Preferences Models, Mean Absolute Percentage Error, Variance of Errors

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.6 No.4, November 12, 2018

  ABSTRACT: Fuzzy regression analysis is an important regression analysis method to predict uncertain information in the real world. In this paper, the input data are crisp with randomness; the output data are trapezoid fuzzy number, and three different risk preferences and chaos optimization algorithm are introduced to establish fuzzy regression model. On the basis of the principle of the minimum total spread between the observed and the estimated values, risk-neutral, risk-averse, and risk-seeking fuzzy regression model are developed to obtain the parameters of fuzzy linear regression model. Chaos optimization algorithm is used to determine the digital characteristic of random variables. The mean absolute percentage error and variance of errors are adopted to compare the modeling results. A stock rating case is used to evaluate the fuzzy regression models. The comparisons with five existing methods show that our proposed method has satisfactory performance.