Goodness of fit and variable selection in the fuzzy multiple linear regression
In performing a fuzzy multiple linear regression model, important topics are: to measure the fitting quality of the model and to find the "best" set of input variables that explain the variation in the observed system responses. In this paper, by considering an exploratory approach, to express the quality of fit of a fuzzy linear regression model, a coefficient of multiple determination R-2 for symmetrical fuzzy variable has been suggested. Furthermore, for overcoming the inconveniences of R-2 an adjusted version of R-2 (denoted by (R) over bar (2)) has been defined. For measuring the fitting performances of the estimated model, a fuzzy extension of another goodness of fit measure, the so-called Mallows index (C-p), has been considered. All the proposed fitting measures have been utilized for selecting suitably the input variables of a fuzzy linear regression model. To this purpose, some variable selection procedures based on R-2, (R) over bar (2) and C-p have been suitably extended in a fuzzy framework. To explain the efficacy of the goodness of fit measures and the variable selection criteria some examples are also shown. (c) 2006 Elsevier B.V. All rights reserved.
exploratory approach coefficient of multiple determination fitting measure variable selection criteria fuzzy linear regression analysis crisp input variables symmetrical fuzzy output variable least-squares approach deviance decomposition adjusted coefficient of multiple determination mallows measure