Bartosz A. Nowak

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The architecture of neuro-fuzzy systems with fuzzy rough sets originally has been developed to process with imprecise data. In this paper, the adaptation of those systems to the missing features case is presented. However, the main considerations concern with methods of learning which could be applied to such systems for approximation tasks. Various methods(More)
One of important methods designed to classify objects with missing feature values are rough neuro-fuzzy classifiers (RNFC). Similarly to neuro-fuzzy systems, they are specific network structures, which can be trained by optimization methods based on gradient descent. However, to the best of our knowledge, there are no publications concerning such way of(More)
The main problem addressed in this paper is to handle adequately imprecision of input data by means of a combination of fuzzy methods with the rough set theory. We will make use of fuzzy rough sets derived as rough approximations of fuzzy antecedent sets by non-singleton fuzzy premise sets in a fuzzy classifier. Adaptation of the parameters of this system(More)