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A Coevolutionary Approach to Optimize Class Boundaries for Multidimensional Classification Problems

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Computational Science and Its Applications – ICCSA 2005 (ICCSA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3483))

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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.

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References

  1. Abe, S., Thawonmas, R.: A fuzzy classifier with ellipsoidal regions. IEEE Trans. on Fuzzy Systems 5, 358–368 (1997)

    Article  Google Scholar 

  2. Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases (1998), Available online via http://www.ics.uci.edu/~mlearn/MLRepository.html

  3. 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)

    Google Scholar 

  4. Haykin, S.: Neural networks: a comprehensive foundation. Prentice-Hall, New Jersey (1994)

    MATH  Google Scholar 

  5. 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)

    Article  Google Scholar 

  6. Kudo, M., Shimbo, M.: Feature Selection Based on the Structural Indices of Categories. Pattern Recognition 26, 891–901 (1993)

    Article  Google Scholar 

  7. Liu, H., Setiono, R.: Incremental feature selection. Applied Intelligence 9, 217–230 (1998)

    Article  Google Scholar 

  8. 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)

    Article  Google Scholar 

  9. 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)

    Article  Google Scholar 

  10. Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufmann, San Mateo (1993)

    Google Scholar 

  11. Simpson, P.K.: Fuzzy min-max neural networks-part1: classification. IEEE Trans. on Neural Networks 3, 776–786 (1992)

    Article  Google Scholar 

  12. Weiss, S.M., Kulikowski, C.A.: Computer systems that learn. Morgan Kaufmann, San Francisco (1991)

    Google Scholar 

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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

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  • 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)

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