NDLI: An Immunological Density-Preserving Approach to the Synthesis of RBF Neural Networks for classification

Content Provider	IEEE Xplore Digital Library
Author	Barra, T.V. Bezerra, G.B. de Castro, L.N. Von Zuben, F.J.
Copyright Year	2006
Description	Author affiliation: Campinas Univ., Campinas (Barra, T.V.; Bezerra, G.B.; de Castro, L.N.; Von Zuben, F.J.)
Abstract	Radial basis function (RBF) neural networks are universal approximators and have been used for a wide range of applications. Aiming at reducing the number of neurons in the hidden layer, for regularization purposes, the center and dispersion of each RBF have to be properly defined by means of competitive learning. Only the output weights will be defined in a supervised manner. One of the drawbacks of such learning methodology, involving unsupervised and supervised learning, is that the centers will be defined so that regions in the input space with a high density of samples tend to be under-represented and those regions with a low density of samples tend to be over-represented. Additionally, few approaches provide a proper and individual indication of the dispersion of each RBF. This paper presents an immune density-preserving algorithm with adaptive radius, called ARIA, to determine the number of centers, their location and the dispersion of each RBF, based only on the available training data set. Considering classification problems, the algorithm to determine the hidden layer is compared to another immune-inspired algorithm called aiNet, K-means and the random choice of centers. The classification accuracy of the final network is compared to another density based approach and a decision tree classifier, C 5.0. The results are reported and analyzed.
Starting Page	929
Ending Page	935
File Size	522574
Page Count	7
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.246785
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Network synthesis Neural networks Neurons Radial basis function networks Prototypes Classification tree analysis Industrial training Computer networks Supervised learning Training data
Content Type	Text
Resource Type	Article

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