Training Radial Basis Function Neural Networks for Classification via Class-Specific Clustering

In training radial basis function neural networks (RBFNNs), the locations of Gaussian neurons are commonly determined by clustering. Training inputs can be clustered on a fully unsupervised manner (input clustering), or some supervision can be introduced, for example, by concatenating the input vect...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 27; no. 12; pp. 2458 - 2471
Main Authors: Raitoharju, Jenni, Kiranyaz, Serkan, Gabbouj, Moncef
Format: Journal Article
Language:English
Published: United States IEEE 01.12.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Centroids
Classification
Clustering
Clustering algorithms
Clustering methods
Computational neuroscience
Heuristic algorithms
Neural networks
Neurons
Optimization
Particle swarm optimization
particle swarm optimization (PSO)
Radial basis function
radial basis function networks (RBFNNs)
supervised learning
Training
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:In training radial basis function neural networks (RBFNNs), the locations of Gaussian neurons are commonly determined by clustering. Training inputs can be clustered on a fully unsupervised manner (input clustering), or some supervision can be introduced, for example, by concatenating the input vectors with weighted output vectors (input-output clustering). In this paper, we propose to apply clustering separately for each class (class-specific clustering). The idea has been used in some previous works, but without evaluating the benefits of the approach. We compare the class-specific, input, and input-output clustering approaches in terms of classification performance and computational efficiency when training RBFNNs. To accomplish this objective, we apply three different clustering algorithms and conduct experiments on 25 benchmark data sets. We show that the class-specific approach significantly reduces the overall complexity of the clustering, and our experimental results demonstrate that it can also lead to a significant gain in the classification performance, especially for the networks with a relatively few Gaussian neurons. Among other applied clustering algorithms, we combine, for the first time, a dynamic evolutionary optimization method, multidimensional particle swarm optimization, and the class-specific clustering to optimize the number of cluster centroids and their locations.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2015.2497286