The paper concerns the architecture of a neuro-fuzzy classifier with fuzzy rough sets which has been developed to process imprecise data. A raw output of such system is an interval which has to be interpreted in terms of classi- fication afterwards. To obtain a credible answer, the interval should be as narrow as possible; however, its width cannot be zero as long as input values are impre- cise. In the paper, we discuss the determination of classifier parameters using the standard gradient learning technique. The effectiveness of the proposed method is confirmed by several simulation experiments.
['Bartosz A. Nowak', 'Robert Nowicki', 'Janusz T. Starczewski', 'Antonino Marvuglia']
The Learning of Neuro-Fuzzy Classifier with Fuzzy Rough Sets for Imprecise Datasets