Abstract
Instance selection methods are very useful data mining tools for dealing with large data sets. There exist many instance selection algorithms capable for significant reduction of training data size for particular classifier without generalization degradation. In opposition to those methods, this paper focuses on general pruning methods which can be successfully applied for arbitrary classification method. Simple but efficient wrapper method based on generalization of Hart’s Condensed Nearest Neighbors rule is presented and impact of this method on classification quality is reported.
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.
Similar content being viewed by others
References
Asuncion, A., Newman, D.J.: UCI machine learning repository (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
DuMouchel, W.: Data squashing: constructing summary data sets. In: Handbook of Massive Data Sets, pp. 579–591. Kluwer Academic Publishers, Norwell (2002)
Grąbczewski, K., Duch, W.: The separability of split value criterion. In: Proceedings of the 5th Conf. on Neural Networks and Soft Computing, pp. 201–208. Polish Neural Network Society, Zakopane (2000)
Grochowski, M., Jankowski, N.: Comparison of Instance Selection Algorithms II. Results and Comments. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 580–585. Springer, Heidelberg (2004)
Grudziński, K., Grochowski, M., Duch, W.: Pruning Classification Rules with Reference Vector Selection Methods. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS, vol. 6113, pp. 347–354. Springer, Heidelberg (2010)
Hart, P.E.: The condensed nearest neighbor rule. IEEE Transactions on Information Theory 14, 515–516 (1968)
Jankowski, N., Grochowski, M.: Comparison of Instances Seletion Algorithms I. Algorithms Survey. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 598–603. Springer, Heidelberg (2004)
Liu, H., Motoda, H.: Instance Selection and Construction for Data Mining. Kluwer Academic Publishers, Norwell (2001)
Nadeau, C., Bengio, Y.: Inference for the generalization error. Machine Learning 52(3), 239–281 (2003)
Skalak, D.B.: Prototype and feature selection by sampling and random mutation hill climbing algorithms. In: Proc. 11th International Conference on Machine Learning, pp. 293–301 (1994)
Smith-Miles, K., Islam, R.: Meta-learning for data summarization based on instance selection method. In: IEEE Congress on Evolutionary Computation, pp. 1–8 (2010)
Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans. Systems, Man and Cybernetics 2, 408–421 (1972)
Wilson, D.R., Martinez, T.R.: Reduction techniques for instance-based learning algorithms. Machine Learning 38(3), 257–286 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Grochowski, M. (2012). Simple Incremental Instance Selection Wrapper for Classification. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29350-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-29350-4_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29349-8
Online ISBN: 978-3-642-29350-4
eBook Packages: Computer ScienceComputer Science (R0)