Title :
A fuzzy-GRNN classifier for pattern recognition
Author :
Verma, Prabha ; Singh, Prashant ; Yadava, R.D.S.
Author_Institution :
Dept. of Phys., Banaras Hindu Univ., Varanasi, India
Abstract :
We present a scheme for combining fuzzy c-means clustering with generalized regression neural network for improving its pattern classification efficiency. The membership grades of data points produced by fuzzy c-means clustering have been used to define weights for data points in the feature space according a logarithmic measure of uncertainty similar to the Shannon´s entropy. The method improves classification results from 1% to 17% for 9 data sets analyzed here.
Keywords :
fuzzy set theory; learning (artificial intelligence); neural nets; pattern classification; pattern clustering; regression analysis; Shannon entropy; data point weights; feature space; fuzzy c-means clustering; fuzzy-GRNN classifier; generalized regression neural network; logarithmic uncertainty measure; membership grades; pattern classification efficiency improvement; pattern recognition; Neural networks; Neurons; Pattern recognition; Principal component analysis; Sensors; Signal processing algorithms; Uncertainty; Ferature vector weighting; Fuzzy uncertainty; Generalized regression neural network calssifier;
Conference_Titel :
Signal Processing and Integrated Networks (SPIN), 2015 2nd International Conference on
Conference_Location :
Noida
Print_ISBN :
978-1-4799-5990-7
DOI :
10.1109/SPIN.2015.7095166
