Evolving an adaptive artificial neural network with a gravitational search algorithm
Data(s) |
01/01/2015
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Resumo |
In this paper, a supervised fuzzy adaptive resonance theory neural network, i.e., Fuzzy ARTMAP (FAM), is integrated with a heuristic Gravitational Search Algorithm (GSA) that is inspired from the laws of Newtonian gravity. The proposed FAM-GSA model combines the unique features of both constituents to perform data classification. The classification performance of FAM-GSA is benchmarked against other state-of-art machine learning classifiers using an artificially generated data set and two real data sets from different domains. Comparatively, the empirical results indicate that FAM-GSA generally is able to achieve a better classification performance with a parsimonious network size, but with the expense of a higher computational load. |
Identificador | |
Idioma(s) |
eng |
Publicador |
Springer |
Relação |
http://dro.deakin.edu.au/eserv/DU:30083049/lim-evolvinganadaptive-2015.pdf http://dro.deakin.edu.au/eserv/DU:30083049/lim-evolvinganadaptive-evid1-2015.pdf http://dro.deakin.edu.au/eserv/DU:30083049/lim-evolvinganadaptive-evid2-2015.pdf http://www.dx.doi.org/10.1007/978-3-319-19857-6_51 |
Direitos |
2015, Springer |
Tipo |
Conference Paper |