Recently, distributed semi-supervised learning (DSSL) algorithms have shown their effectiveness in leveraging unlabeled samples over interconnected networks, where agents cannot share original data with each other and can only communicate non-sensitive information neighbors. However, existing DSSL cope uncertainties may suffer from high computation communication overhead problems. To handle the...

Since Tanaka et al. in 1982 proposed a study in linear regression with a fuzzy model, fuzzy regression analysis has been widely studied and applied in various areas. However, Tanaka’s approach may give an incorrect interpretation of the fuzzy linear regression results when outliers are present in the data set. To handle the outlier problem, we propose an omission approach for Tanaka’s linear pr...

This paper revisits interval-valued fuzzy regression and proposes a new unified framework to address type-1 type-2 models. The focuses on two main objectives. First, some philosophical methodological reflections about (IV-T1FR) (IV-T2FR) are discussed analyzed. These aim at positioning avoid misinterpretations that may sometimes lead erroneous or ambiguous considerations in practical applicatio...

An expert may experience difficulties in decision making when evaluating alternatives through a single assessment value hesitant environment. A fuzzy linear regression model (FLRM) is used for decision-making purposes, but this entirely unreasonable the presence of information. In order to overcome issue, paper, we define (HFLRM) account multicriteria (MCDM) problems The HFLRM provides an alter...

In this paper, we deal with the ridge-type estimator for fuzzy nonlinear regression models using fuzzy numbers and Gaussian basis functions. Shrinkage regularization methods are used in linear and nonlinear regression models to yield consistent estimators. Here, we propose a weighted ridge penalty on a fuzzy nonlinear regression model, then select the number of basis functions and smoothing par...