Abstract
One of the most important feature of the Neural Gas is its ability to preserve the topology in the projection of highly dimensional input spaces to lower dimensions vector quantizations. For this reason, the Neural Gas has proven to be a valuable tool in data mining applications.
In this paper an incremental ensemble method for the combination of various Neural Gas models is proposed. Several models are trained with bootstrap samples of the data, the “codebooks” with similar Voronoi polygons are merged in one fused node and neighborhood relations are established by linking similar fused nodes. The aim of combining the Neural Gas is to improve the quality and robustness of the topological representation of the single model. We have called this model Fusion-NG.
Computational experiments show that the Fusion-NG model effectively preserves the topology of the input space and improves the representation of the single Neural Gas model. Furthermore, the Fusion-NG explicitly shows the neighborhood relations of it prototypes. We report the performance results using synthetic and real datasets, the latter obtained from a benchmark site.
This work was supported by Research Grant Fondecyt 1061201, 1070220 and DGIP-UTFSM.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998)
Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)
Efron, B.: Bootstrap methods: another look at the jacknife. The Annals of Statistics 7, 1–26 (1979)
Kohonen, T.: Self-Organizing Maps, Springer Series in Information Sciences, vol, 3rd extended edn. Springer Series in Information Sciences, vol. 30. Springer, Heidelberg (2001)
Kuncheva, L.: Switching between selection and fusion in combining classifiers: An experiment. IEEE Trans. on System Man, And Cybernetics – Part B 32(2), 146–156 (2002)
Kuncheva, L.: Combining pattern classifiers: Methods and algorithms. Wiley, Chichester (2004)
Martinetz, T., Berkovich, S., Schulten, K.: Neural-gas network for vector quantization and its application to time-series prediction. IEEE Trans. on Neural Networks 4(4), 558–568 (1993)
Polikar, R.: Ensemble based systems in decision making. IEEE Circuits and Systems Magazine 6(3), 21–45 (2006)
Saavedra, C., Moreno, S., Salas, R., Allende, H.: Robustness analysis of the neural gas learning algorithm. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds.) CIARP 2006. LNCS, vol. 4225, pp. 559–568. Springer, Heidelberg (2006)
Schapire, R.: The boosting approach to machine learning: An overview (2001)
Seiffert, U., Jain, L. (eds.): Self-organizing neural networks: Recent advances and applications. Studies in Fuzziness and Soft Computing, vol. 78. Springer, Heidelberg (2002)
Su, M., Chang, H.: Fast self-organizing feature map algorithm. IEEE Trans. on Neural Networks 11(3), 721–733 (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Moreno, S., Allende, H., Salas, R., Saavedra, C. (2007). Fusion of Neural Gas. In: Mira, J., Álvarez, J.R. (eds) Bio-inspired Modeling of Cognitive Tasks. IWINAC 2007. Lecture Notes in Computer Science, vol 4527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73053-8_56
Download citation
DOI: https://doi.org/10.1007/978-3-540-73053-8_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73052-1
Online ISBN: 978-3-540-73053-8
eBook Packages: Computer ScienceComputer Science (R0)