Nonlinear nonnegative multiregressions based on Choquet integrals
Using a nonadditive set function to describe the interaction among attributes, a new nonlinear nonnegative multiregression is established based on Choquet integrals with respect to the set function. Regarding the values of the set function as unknown regression parameters, an evolutionary computation can be used to determine them when necessary data are available. Such a model is a generalization of the traditional linear multiregression. It provides an effective regression tool in some real problems where the linear multiregression model and the second-order multiregression model fail. This new method has a wide applicability in the areas of information fusion and data mining, as well as in the areas of decision making, image processing, pattern recognition, medical and industrial diagnoses, and expert systems. (C) 2000 Elsevier Science Inc. All rights reserved.
choquet integrals information fusion data mining nonlinear regressions nonnegative monotone set functions optimization evolutionary algorithms