Abstract
In this paper ensemble techniques have been applied in the frame of topology preserving mappings in two applications: classification and visualization. These techniques are applied for the first time to the ViSOM and their performance is compared with ensemble combination of some other topology preserving mapping such as the SOM or the MLSIM. Several methods to obtain a meaningful combination of the components of an ensemble are presented and tested together with the existing ones in order to identify the best performing method in the applications of these models.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Kohonen, T., Lehtio, P., Rovamo, J., Hyvarinen, J., Bry, K., Vainio, L.: A Principle of Neural Associative Memory. Neuroscience 2, 1065–1076 (1977)
Vesanto, J.: Data Mining Techniques Based on the Self-Organizing Map. Engineering Physics and Mathematics. Helsinki University of Technology. Espoo, Finland (1997)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms (2004)
Ron, M., Gunnar, R.: An Introduction to Boosting and Leveraging. In: Advanced Lectures on Machine Learning: Machine Learning Summer School 2002, Canberra, Australia, Revised Lectures, February 11-22, 2002, pp. 118–183 (2003)
Kohonen, T.: The Self-Organizing Map. Neurocomputing 21, 1–6 (1998)
Kaski, S.: Data Exploration Using Self-Organizing Maps. Department of Computer Science and Engineering. Helsinki University of Technology. Espoo, Finland (1997)
Yin, H.: Data Visualisation and Manifold Mapping Using the Visom. Neural Networks 15, 1005–1016 (2002)
Yin, H.: Visom - a Novel Method for Multivariate Data Projection and Structure Visualization. IEEE Transactions on Neural Networks 13, 237–243 (2002)
Fyfe, C.: A Scale-Invariant Feature Map. Network: Computation in Neural Systems 7, 269–275 (1996)
Corchado, E., Fyfe, C.: The Scale Invariant Map and Maximum Likelihood Hebbian Learning. In: International Conference on Knowledge-Based and Intelligent Information and Engineering System (2002)
Fyfe, C., Corchado, E.: Maximum Likelihood Hebbian Rules. In: European Symposium on Artificial Neural Networks, ESANN (2002)
Corchado, E., MacDonald, D., Fyfe, C.: Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit. Data Mining and Knowledge Discovery 8, 203–225 (2004)
Kraaijveld, M.A., Mao, J., Jain, A.K.: A Nonlinear Projection Method Based on Kohonen’s Topology Preserving Maps. IEEE Transactions on Neural Networks 6, 548–559 (1995)
Heskes, T.: Balancing between Bagging and Bumping. In: Advances in Neural Information Processing Systems, vol. 9, Proceedings of the 1996 Conference, pp. 466–472 (1997)
Ruta, D., Gabrys, B.: A Theoretical Analysis of the Limits of Majority Voting Errors for Multiple Classifier Systems. Pattern Analysis and Applications 5, 333–350 (2002)
Petrakieva, L., Fyfe, C.: Bagging and Bumping Self Organising Maps. Computing and Information Systems (2003)
Corchado, E.S., Baruque, B., Gabrys, B.: Maximum Likelihood Topology Preserving Ensembles. In: Corchado, E.S., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 1434–1442. Springer, Heidelberg (2006)
Georgakis, A., Li, H., Gordan, M.: An Ensemble of Som Networks for Document Organization and Retrieval. In: Int. Conf. on Adaptive Knowledge Representation and Reasoning (AKRR’05), p. 6 (2005)
Breiman, L.: Bagging Predictors. Machine Learning 24, 123–140 (1996)
Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: Uci Repository of Machine Learning Databases. University of California, Irvine, Dept. of Information and Computer Sciences (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Baruque, B., Corchado, E., Yin, H. (2007). ViSOM Ensembles for Visualization and Classification. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_29
Download citation
DOI: https://doi.org/10.1007/978-3-540-73007-1_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73006-4
Online ISBN: 978-3-540-73007-1
eBook Packages: Computer ScienceComputer Science (R0)