Open AccessUse of Shapley value for selecting centres in RBF neural regressors
Author(s) -
Coelho A.L.V.,
Maia J.E.B.,
Sandes N.C.
Publication year - 2014
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2014.0345
Subject(s) - benchmark (surveying) , shapley value , artificial neural network , selection (genetic algorithm) , computer science , value (mathematics) , perspective (graphical) , radial basis function , artificial intelligence , mathematical optimization , game theory , machine learning , mathematics , mathematical economics , geography , geodesy
The problem of centre selection in radial basis function neural networks (RBFNNs) is re‐examined and tackled through a cooperative game theoretic perspective. By resorting to the notion of Shapley value, the approach ranks candidate centres (modelled as game players) for the RBFNN's hidden layer based on a sampled estimation of their marginal contribution to the cross‐validation training error. Results achieved on benchmark regression problems are reported, whereby it has been shown that the proposed approach improves on the results delivered by the two well‐known algorithms.