Title :
A two-level classification scheme trained by a fuzzy neural network
Author :
Kuncheva, Ludmila I. ; Mitra, Sushmita
Author_Institution :
Bulgarian Acad. of Sci., Sofia, Bulgaria
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
Keywords :
pattern classification; classification accuracy; feature space; fuzzy neural network; linguistically interpretable inputs; two-level pattern classification; Aggregates; Electronic mail; Fuzzy neural networks; Multilayer perceptrons; Neural networks; Pattern recognition; Space technology;
Conference_Titel :
Pattern Recognition, 1994. Vol. 2 - Conference B: Computer Vision & Image Processing., Proceedings of the 12th IAPR International. Conference on
Conference_Location :
Jerusalem
Print_ISBN :
0-8186-6270-0
DOI :
10.1109/ICPR.1994.576984