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http://dx.doi.org/10.5391/JKIIS.2012.22.6.735

Design of Optimized Radial Basis Function Neural Networks Classifier with the Aid of Principal Component Analysis and Linear Discriminant Analysis  

Kim, Wook-Dong (Department of Electrical Engineering, The University of Suwon)
Oh, Sung-Kwun (Department of Electrical Engineering, The University of Suwon)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.22, no.6, 2012 , pp. 735-740 More about this Journal
Abstract
In this paper, we introduce design methodologies of polynomial radial basis function neural network classifier with the aid of Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA). By minimizing the information loss of given data, Feature data is obtained through preprocessing of PCA and LDA and then this data is used as input data of RBFNNs. The hidden layer of RBFNNs is built up by Fuzzy C-Mean(FCM) clustering algorithm instead of receptive fields and linear polynomial function is used as connection weights between hidden and output layer. In order to design optimized classifier, the structural and parametric values such as the number of eigenvectors of PCA and LDA, and fuzzification coefficient of FCM algorithm are optimized by Artificial Bee Colony(ABC) optimization algorithm. The proposed classifier is applied to some machine learning datasets and its result is compared with some other classifiers.
Keywords
Radial Basis Function Neural Networks; Principal Component Analysis; Linear Discriminant Analysis; Artificial Bee Colony; Fuzzy C-Means Clustering;
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