We formulate regression as maximizing the minimum probability (Ω) that the true regression function is within ±2 of the regression model. Our framework starts by posing regression as a binary classification problem, such that a solution to this single classification problem directly solves the original regression problem. Minimax probability machine classification (Lanckriet et al., 2002a) is u...

Since Tanaka et al. (1982) proposed a study of linear regression analysis with a fuzzy model, fuzzy regression analysis has been widely studied and applied in a variety of substantive areas. Regression analysis in the case of heterogeneity of observations is commonly presented in practice. The authors' main goal is to apply fuzzy clustering techniques to fuzzy regression analysis. Fuzzy cluster...

Solutions of complex problems require intelligent systems that combine knowledge, techniques and methodologies, from different sources, considering environmental changes, for decision support improvement. Thus, it became necessary to apply robust methodologies to characterize the interaction among climatic variables related to epidemic progress. The objective of the present work was to evaluate...

fuzzy linear regression models are used to obtain an appropriate linear relation between a dependent variable and several independent variables in a fuzzy environment. several methods for evaluating fuzzy coefficients in linear regression models have been proposed. the first attempts at estimating the parameters of a fuzzy regression model used mathematical programming methods. in this the...