Abstract
In the paper a novel method of classification is presented. It is a combination of unsupervised and supervised techniques. First, the method divides the set of learning patterns into smaller ones in the clustering process. At the end of this phase a hierarchical structure of Self Organizing Map is obtained. Then for the leaves the classification rules are searched. To this end Bee Algorithm is used. The accuracy of the method was evaluated in an experimental way with the use of benchmark data sets and compared with the result of other methods.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Duch, W.: Datasets Used for Classification: Comparison of Results (2009), http://www.is.umk.pl/projects/datasets.html
Haykin, S.: Self-organizing maps. Neural networks - a Comprehensive Foundation, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)
Karaboga, D.: An Idea Based On Honey Bee Swarm for Numerical Optimization. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)
Kotsiantis, S.B.: Supervised Machine Learning: A Review of Classification Techniques. Informatica 31, 249–268 (2007)
Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)
Sullivan, K., Luke, S.: Evolving Kernels for Support Vector Machine Classification. In: Genetic And Evolutionary Computation Conference Archive Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 1702–1707 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Markowska-Kaczmar, U., Switek, T. (2009). Combined Unsupervised-Supervised Classification Method. In: Velásquez, J.D., Ríos, S.A., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2009. Lecture Notes in Computer Science(), vol 5712. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04592-9_107
Download citation
DOI: https://doi.org/10.1007/978-3-642-04592-9_107
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04591-2
Online ISBN: 978-3-642-04592-9
eBook Packages: Computer ScienceComputer Science (R0)