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Looking for Prototypes by Genetic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4153))

Abstract

In this paper we propose a new genetic programming based approach for prototype generation in Pattern Recognition problems. Prototypes consist of mathematical expressions and are encoded as derivation trees. The devised system is able to cope with classification problems in which the number of prototypes is not a priori known. The approach has been tested on several problems and the results compared with those obtained by other genetic programming based approaches previously proposed.

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References

  1. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & sons, Inc., Chichester (2001)

    MATH  Google Scholar 

  2. Zhang, G.P.: Neural networks for classification: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C 30(4), 451–462 (2000)

    Article  Google Scholar 

  3. Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)

    Google Scholar 

  4. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology. In: Control and Artificial Intelligence. MIT Press, Cambridge (1992)

    Google Scholar 

  5. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Amsterdam (1989)

    MATH  Google Scholar 

  6. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  7. Koza, J.R.: Genetic programming II: automatic discovery of reusable programs. MIT Press, Cambridge (1994)

    MATH  Google Scholar 

  8. Sette, S., Boullart, L.: Genetic programming: principles and applications. Engineering Applications of Artificial Intelligence 14, 727–736 (2001)

    Article  Google Scholar 

  9. Bastian, A.: Identifying fuzzy models utilizing genetic programming. Fuzzy Sets and Systems 113, 333–350 (2000)

    Article  MATH  Google Scholar 

  10. Muni, D.P., Pal, N.R., Das, J.: A novel approach to design classifiers using genetic programming. IEEE Trans. Evolutionary Computation 8, 183–196 (2004)

    Article  Google Scholar 

  11. Agnelli, D., Bollini, A., Lombardi, L.: Image classification: an evolutionary approach. Pattern Recognition Letters 23, 303–309 (2002)

    Article  MATH  Google Scholar 

  12. Cordella, L.P., De Stefano, C., Fontanella, F., Marcelli, A.: Genetic programming for generating prototypes in classification problems. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1149–1155. IEEE Press, Los Alamitos (2005)

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© 2006 Springer-Verlag Berlin Heidelberg

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Cordella, L.P., De Stefano, C., Fontanella, F., Marcelli, A. (2006). Looking for Prototypes by Genetic Programming. In: Zheng, N., Jiang, X., Lan, X. (eds) Advances in Machine Vision, Image Processing, and Pattern Analysis. IWICPAS 2006. Lecture Notes in Computer Science, vol 4153. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11821045_16

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  • DOI: https://doi.org/10.1007/11821045_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37597-5

  • Online ISBN: 978-3-540-37598-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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