Abstract
This paper proposes a coevolutionary classification method to discover classifiers for multidimensional pattern classification problems with continuous features. The classification problems may be decomposed into two sub-problems, which are feature selection and classifier adaptation. A coevolutionary classification method is designed by coordinating the two sub-problems, whose performances are affected by each other. The proposed method establishes a group of partial sub-regions, defined by regional feature set, and then fits a finite number of classifiers to the data pattern by combining a genetic algorithm and a local adaptation algorithm in every sub-region. A cycle of the cooperation loop is completed by evolving the sub-regions based on the evaluation results of the fitted classifiers located in the corresponding sub-regions. The classifier system has been tested with well-known data sets from the UCI machine-learning database, showing superior performance to other methods such as the nearest neighbor, decision tree, and neural networks.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Abe, S., Thawonmas, R.: A fuzzy classifier with ellipsoidal regions. IEEE Trans. on Fuzzy Systems 5, 358–368 (1997)
Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases (1998), Available online via http://www.ics.uci.edu/~mlearn/MLRepository.html
Eshelman, L.J.: The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In: Rawlins, G.J.E. (ed.) Foundations of genetic algorithms, pp. 265–283. Morgan Kaufman, San Mateo (1991)
Haykin, S.: Neural networks: a comprehensive foundation. Prentice-Hall, New Jersey (1994)
Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: a review. IEEE Trans. on Pattern Analysis and Machine Intelligence 22, 4–37 (2000)
Kudo, M., Shimbo, M.: Feature Selection Based on the Structural Indices of Categories. Pattern Recognition 26, 891–901 (1993)
Liu, H., Setiono, R.: Incremental feature selection. Applied Intelligence 9, 217–230 (1998)
Nolan, J.R.: Computer systems that learn: an empirical study of the effect of noise on the performance of three classification methods. Expert Systems with Applications 23, 39–47 (2002)
Pal, S.K., Bandyopadhyay, S., Murthy, C.A.: Genetic algorithms for generation of class boundaries. IEEE Trans. on Systems, Man, and Cybernetics-Part B: Cybernetics 28, 816–828 (1998)
Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufmann, San Mateo (1993)
Simpson, P.K.: Fuzzy min-max neural networks-part1: classification. IEEE Trans. on Neural Networks 3, 776–786 (1992)
Weiss, S.M., Kulikowski, C.A.: Computer systems that learn. Morgan Kaufmann, San Francisco (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, KK. (2005). A Coevolutionary Approach to Optimize Class Boundaries for Multidimensional Classification Problems. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424925_46
Download citation
DOI: https://doi.org/10.1007/11424925_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25863-6
Online ISBN: 978-3-540-32309-9
eBook Packages: Computer ScienceComputer Science (R0)