A multiobjective analysis of adaptive clustering algorithms for the definition of RBF neural network centers in regression problems

Authors:
Rosana Veroneze;André R. Gonçalves;Fernando J. Von Zuben
Affiliations:
School of Electrical and Computer Engineering, University of Campinas, Campinas, São Paulo, Brazil;School of Electrical and Computer Engineering, University of Campinas, Campinas, São Paulo, Brazil;School of Electrical and Computer Engineering, University of Campinas, Campinas, São Paulo, Brazil
Venue:
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Year:
2012

Citing 6
Cited 1

Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)

Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Studying possibility in a clustering algorithm for RBFNN design for function approximation

Neural Computing and Applications
Adaptive radius immune algorithm for data clustering

ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation
Localized Generalization Error Model and Its Application to Architecture Selection for Radial Basis Function Neural Network

IEEE Transactions on Neural Networks

The influence of supervised clustering for RBFNN centers definition: a comparative study

ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

A variety of clustering algorithms have been applied to determine the internal structure of Radial Basis Function Neural Networks (RBFNNs). k-means algorithm is one of the most common choice for this task, although, like many other clustering algorithms, it needs to receive the number of prototypes a priori. This is a nontrivial procedure, mainly for real-world applications. An alternative is to use algorithms that automatically determine the number of prototypes. In this paper, we performed a multiobjective analysis involving three of these algorithms, which are: Adaptive Radius Immune Algorithm (ARIA), Affinity Propagation (AP), and Growing Neural Gas (GNG). For each one, the parameters that most influence the resulting number of prototypes composed the decision space, while the RBFNN RMSE and the number of prototypes formed the objective space. The experiments found that ARIA solutions achieved the best results for the multiobjective metrics adopted in this paper.