Abstract
The design of nearest neighbour classifiers is very dependent from some crucial parameters involved in learning, like the number of prototypes to use, the initial localization of these prototypes, and a smoothing parameter. These parameters have to be found by a trial and error process or by some automatic methods. In this work, an evolutionary approach based on Nearest Neighbour Classifier (ENNC), is described. Main property of this algorithm is that it does not require any of the above mentioned parameters. The algorithm is based on the evolution of a set of prototypes that can execute several operators in order to increase their quality in a local sense, and emerging a high classification accuracy for the whole classifier.
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
D. Aha and K. Kibler. Instance-based learning algorithms. Machine Learning, 6:37–66, 1991.
Pietro Burrascano. Learning vector quantization for the probabilistic neural network. IEEE Transactions on Neural Networks, 2(4):458–461, July 1991.
Richard O. Duda and Peter E. Hart. Pattern Classification and Scene Analysis. John Wiley And Sons, 1973.
Eibe Frank and Ian H. Witten. Generating accurate rule sets without global optimization. In Proceedings of the Fifteenth International Conference on Machine Learnin, 1998.
Teuvo Kohonen. Self-Organization and Associative Memory. Springer, Berlin, Heidelberg, 1984. 3rd ed. 1989.
K. Z. Mao, K.-C. Tan, and W. Ser. Probabilistic neural-network structure determination for pattern classification. IEEE Transactions on Neural Networks, 11(4):1009–1016, July 2000.
J. Ross Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
I. H. Witten and E. Frank. Data Mining. Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, 2000.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Isasi, P., Fernandez, F. (2003). Evolutionary Approach to Overcome Initialization Parameters in Classification Problems. In: Mira, J., Álvarez, J.R. (eds) Computational Methods in Neural Modeling. IWANN 2003. Lecture Notes in Computer Science, vol 2686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44868-3_33
Download citation
DOI: https://doi.org/10.1007/3-540-44868-3_33
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40210-7
Online ISBN: 978-3-540-44868-6
eBook Packages: Springer Book Archive