A two-level classification scheme trained by a fuzzy neural network

Abstract

A two-level pattern classification scheme is considered. At its first level the scheme labels the input object as "doubtful" or "certain" and at the second one applies the respective classification rule. Complicating the classifier in such a way the authors aim at a more accurate result than that obtained through either of the classification rules itself. A fuzzy neural network with linguistically interpretable inputs has been applied to detect the boundaries of the "doubtful" region(s) in the feature space. A fuzzy k-nearest neighbors rule with k=1 and k=5 has been used at the second level for the "doubtful" and "certain" regions, respectively. The idea has been demonstrated on a generated data set (two separable classes with uniform distribution). The results show the tendency of improvement of the classification accuracy.