Abstract
In this article different approximations of a local classifier algorithm are described and compared. The classification algorithm is composed by two different steps. The first one consists on the clustering of the input data by means of three different techniques, specifically a k-means algorithm, a Growing Neural Gas (GNG) and a Self-Organizing Map (SOM). The groups of data obtained are the input to the second step of the classifier, that is composed of a set of one-layer neural networks which aim is to fit a local model for each cluster. The three different approaches used in the first step are compared regarding several parameters such as its dependence on the initial state, the number of nodes employed and its performance. In order to carry out the comparative study, two artificial and three real benchmark data sets were employed.
This work has been funded in part by projects PGIDT05TIC10502PR of the Xunta de Galicia and TIN2006-02402 of the Ministerio de Educación y Ciencia, Spain (partially supported by the European Union ERDF).
This is a preview of subscription content, log in via an institution to check access.
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
Hecht-Nielsen, R.: Neurocomputing. Addison-Wesley, Reading, MA (1990)
Ridella, S., Rovetta, S., Zunino, R.: The k-winner machine model. In: IJCNN 2000. Int. Joint Conference on Neural Networks, vol. 1, pp. 106–111. IEEE, Los Alamitos (2000)
Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2004)
Carpenter, G., Grossberg, S.: The art of adaptive pattern recognition by a self-organizing neural network. IEEE Computer 21(3), 77–88 (1988)
Kohonen, T.: Self-organizing maps. Springer, Heidelberg (2001)
Broomhead, D.S., Lowe, D.: Multivariable functional interpolation and adaptive networks. Computer Systems 2, 321–355 (1988)
Bottou, L., Vapnik, V.: Local learning algorithms. Neural Computation 4, 888–900 (1992)
Rumelhart, D.E., Hinton, G.E., Willian, R.J.: Learning representations of back-propagation errors. Nature 323, 533–536 (1986)
Moller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6, 525–533 (1993)
Hagan, M.T., Menhaj, M.: Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 5(6), 989–993 (1994)
Castillo, E., Fontenla-Romero, O., Alonso-Betanzos, A., Guijarro-Berdiñas, B.: A global optimum approach for one-layer neural networks. Neural Computation 14(6), 1429–1449 (2002)
Newman, D., Hettich, S., Blake, C., Merz, C.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: 5-th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)
Fritzke, B.: A growing neural gas network learns topologies. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.) Advances in Neural Information Processing Systems 7 (NIPS 1994), pp. 625–632. MIT Press, Cambridge, MA (1995)
Rodríguez-Pena, R.M., Pérez-Sánchez, B., Fontenla-Romero, O.: A novel local classification method using growing neural gas and proximal support vector machines. In: IJCNN 2007. Int. Joint Conference on Neural Networks, IEEE, Los Alamitos (2007)
Computational Intelligence Laboratory, Department of Informatics, Nicolaus Copernicus University: Datasets used for classification: comparison of results (Last access July 17, 2007), http://www.fizyka.umk.pl/kmk/projects/datasets.html
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gago-Pallares, Y., Fontenla-Romero, O., Alonso-Betanzos, A. (2007). A Comparative Study of Local Classifiers Based on Clustering Techniques and One-Layer Neural Networks. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-77226-2_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77225-5
Online ISBN: 978-3-540-77226-2
eBook Packages: Computer ScienceComputer Science (R0)