Abstract
Several methods were proposed to reduce the number of instances (vectors) in the learning set. Some of them extract only bad vectors while others try to remove as many instances as possible without significant degradation of the reduced dataset for learning. Several strategies to shrink training sets are compared here using different neural and machine learning classification algorithms. In part II (the accompanying paper) results on benchmarks databases have been presented.
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
Duda, R.O., Hart, P.E., Stork, D.G.: Patter Classification and Scene Analysis, 2nd edn. Wiley, Chichester (1997)
Aha, D.W., Kibler, D., Albert, M.K.: Aha. Machine Learning 6, 37–66 (1991)
Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. Institute of Electrical and Electronics Engineers Transactions on Information Theory 13, 21–27 (1967)
Hart, P.E.: The condensed nearest neighbor rule. IEEE Transactions on Information Theory 14, 515–516 (1968)
Ritter, G.L., Woodruff, H.B., Lowry, S.R., Isenhour, T.L.: An algorithm for a selective nearest neighbor decision rule. IEEE Transactions on Information Theory 21, 665–669 (1975)
Gates, G.: The reduced nearest neighbor rule. IEEE Transactions on Information Theory 18, 431–433 (1972)
Wilson, D.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man, and Cybernetics 2, 408–421 (1972)
Cameron-Jones, R.M.: Instance selection by encoding length heuristic with random mutation hill climbing. In: Proceedings of the Eighth Australian Joint Conference on Artificial Intelligence, pp. 99–106 (1995)
Bhattacharya, B.K., Poulsen, R.S., Toussaint, G.T.: Application of proximity graphs to editing nearest neighbor decision rule. In: International Symposium on Information Theory, Santa Monica (1981)
Brighton, H., Mellish, C.: Advances in instance selection for instance-based learning algorithms. Data Mining and Knowledge Discovery 6, 153–172 (2002)
Skalak, D.B.: Prototype and feature selection by sampling and random mutation hill climbing algorithms. In: International Conference on Machine Learning, pp. 293–301 (1994)
Schölkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)
Grab̧czewski, K., Duch, W.: A general purpose separability criterion for classification systems. In: 4th Conference on Neural Networks and Their Applications, pp. 203–208. Polish Neural Networks Society, Zakopane (1999)
Adamczak, R., Duch, W., Jankowski, N.: New developments in the feature space mapping model. In: Third Conference on Neural Networks and Their Applications, pp. 65–70. Polish Neural Networks Society, Kule (1997)
Jankowski, N., Kadirkamanathan, V.: Statistical control of RBF-like networks for classification. In: 7th International Conference on Artificial Neural Networks, pp. 385–390. Springer, Lausanne (1997)
Tomek, I.: An experiment with the edited nearest-neighbor rule. IEEE Transactions on Systems, Man, and Cybernetics 6, 448–452 (1976)
Grochowski, M.: Wybór wektorów referencyjnych dla wybranych method klasyfikacji. Master’s thesis, Department of Informatics, Nicholas Copernicus University, Poland (2003)
Jankowski, N.: Data regularization. In: Rutkowski, L., Tadeusiewicz, R. (eds.) Neural Networks and Soft Computing, Zakopane, Poland, pp. 209–214 (2000)
Wilson, D.R., Martinez, T.R.: Reduction techniques for instance-based learning algorithms. Machine Learning 38, 257–286 (2000)
Kohonen, T.: Learning vector quantization for pattern recognition. Technical Report TKK-F-A601, Helsinki University of Technology, Espoo, Finland (1986)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jankowski, N., Grochowski, M. (2004). Comparison of Instances Seletion Algorithms I. Algorithms Survey. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_90
Download citation
DOI: https://doi.org/10.1007/978-3-540-24844-6_90
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22123-4
Online ISBN: 978-3-540-24844-6
eBook Packages: Springer Book Archive