The learning of neuro-fuzzy classifier with fuzzy rough sets for imprecise datasets

Abstract

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 classification 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 imprecise. 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. © 2014 Springer International Publishing.