Paper Info
Title
A multiobjective analysis of adaptive clustering algorithms for the definition of RBF neural network centers in regression problems
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.
Year
DOI
Venue
2012
10.1007/978-3-642-32639-4_16
IDEAL
Keywords
Field
DocType
neural gas,multiobjective metrics,objective space,resulting number,rbf neural network center,multiobjective analysis,decision space,adaptive radius immune algorithm,aria solution,adaptive clustering algorithm,regression problem,clustering algorithm,radial basis function neural
Pattern recognition,Affinity propagation,Radial basis function neural,Computer science,A priori and a posteriori,Mean squared error,Artificial intelligence,Regression problems,Cluster analysis,Artificial neural network,Neural gas,Machine learning
Conference
Citations 
PageRank 
References 
1
0.37
5
Authors
3
Name
Order
Citations
PageRank
Rosana Veroneze162.51
André Ricardo Gonçalves2166.43
Fernando J. Von Zuben383181.83