Automatic Design of ANNs by Means of GP for Data Mining Tasks: Iris Flower Classification Problem

  • Daniel Rivero
  • Juan Rabuñal
  • Julián Dorado
  • Alejandro Pazos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4431)

Abstract

This paper describes a new technique for automatically developing Artificial Neural Networks (ANNs) by means of an Evolutionary Computation (EC) tool, called Genetic Programming (GP). This paper also describes a practical application in the field of Data Mining. This application is the Iris flower classification problem. This problem has already been extensively studied with other techniques, and therefore this allows the comparison with other tools. Results show how this technique improves the results obtained with other techniques. Moreover, the obtained networks are simpler than the existing ones, with a lower number of hidden neurons and connections, and the additional advantage that there has been a discrimination of the input variables. As it is explained in the text, this variable discrimination gives new knowledge to the problem, since now it is possible to know which variables are important to achieve good results.

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References

  1. 1.
    Haykin, S.: Neural Networks, 2nd edn. Prentice Hall, Englewood Cliffs (1999)MATHGoogle Scholar
  2. 2.
    Rabuñal, J.R., Dorado, J. (eds.): Artificial Neural Networks in Real-Life Applications. Idea Group Inc., Hershey (2005)Google Scholar
  3. 3.
    Fisher, R.A.: The use of multiple measurements in taxonomic problems. Annals of Eugenics, 179–188 (1936)Google Scholar
  4. 4.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)MATHGoogle Scholar
  5. 5.
    Rivero, D., Rabuñal, J.R., Dorado, J., Pazos, A.: Time Series Forecast with Anticipation using Genetic Programming. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 968–975. Springer, Heidelberg (2005)Google Scholar
  6. 6.
    Bot, M.: Application of Genetic Programming to Induction of Linear Classification Trees. Final Term Project Report, Vrije Universiteit, Amsterdam (1999)Google Scholar
  7. 7.
    Rabuñal, J.R., Dorado, J., Puertas, J., Pazos, A., Santos, A., Rivero, D.: Prediction and Modelling of the Rainfall-Runoff Transformation of a Typical Urban Basin using ANN and GP. Applied Artificial Intelligence (2003)Google Scholar
  8. 8.
    Sutton, R.S.: Two problems with backpropagation and other steepest-descent learning procedure for networks. In: Proc. 8th Annual Conf. Cognitive Science Society, pp. 823–831. Lawrence Erlbaum, Hillsdale (1986)Google Scholar
  9. 9.
    Janson, D.J., Frenzel, J.F.: Training product unit neural networks with genetic algorithms. IEEE Expert 8, 26–33 (1993)CrossRefGoogle Scholar
  10. 10.
    Greenwood, G.W.: Training partially recurrent neural networks using evolutionary strategies. IEEE Trans. Speech Audio Processing 5, 192–194 (1997)CrossRefGoogle Scholar
  11. 11.
    Alba, E., Aldana, J.F., Troya, J.M.: Fully automatic ANN design: A genetic approach. In: Mira, J., Cabestany, J., Prieto, A.G. (eds.) IWANN 1993. LNCS, vol. 686, pp. 399–404. Springer, Heidelberg (1993)Google Scholar
  12. 12.
    Kitano, H.: Designing neural networks using genetic algorithms with graph generation system. Complex Systems 4, 461–476 (1990)MATHGoogle Scholar
  13. 13.
    Yao, X., Liu, Y.: Towards designing artificial neural networks by evolution. Appl. Math. Computation 91(1), 83–90 (1998)MATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Harp, S.A., Samad, T., Guha, A.: Toward the genetic synthesis of neural networks. In: Schafer, J.D. (ed.) Proc. 3rd Int. Conf. Genetic Algorithms and Their Applications, pp. 360–369. Morgan Kaufmann, San Mateo (1989)Google Scholar
  15. 15.
    Nolfi, S., Parisi, D.: Evolution of Artificial Neural Networks. In: Handbook of brain theory and neural networks, 2nd edn., pp. 418–421. MIT Press, Cambridge (2002)Google Scholar
  16. 16.
    Turney, P., Whitley, D., Anderson, R.: Special issue on the baldwinian effect. Evolutionary Computation 4(3), 213–329 (1996)CrossRefGoogle Scholar
  17. 17.
    Montana, D.J.: Strongly typed genetic programming. Evolutionary Computation 3(2), 199–200 (1995)CrossRefGoogle Scholar
  18. 18.
    Mertz, C.J., Murphy, P.M.: UCI repository of machine learning databases (2002), http://www-old.ics.uci.edu/pub/machine-learning-databases
  19. 19.
    Cantú-Paz, E., Kamath, C.: An Empirical Comparison of Combinations of Evolutionary Algorithms and Neural Networks for Classification Problems. IEEE Transactions on systems, Man and Cybernetics – Part B: Cybernetics, 915–927 (2005)Google Scholar
  20. 20.
    Herrera, F., Hervás, C., Otero, J., Sánchez, L.: Un estudio empírico preliminar sobre los tests estadísticos más habituales en el aprendizaje automático. In: Giraldez, R., Riquelme, J.C., Aguilar, J.S. (eds.) Tendencias de la Minería de Datos en España, Red Española de Minería de Datos y Aprendizaje, pp. 403–412 (2004)Google Scholar
  21. 21.
    Gruau, F.: Genetic Micro Programming of Neural Networks. In: Kinnear, K. (ed.) Advances in Genetic Programming, pp. 495–518. MIT Press, Cambridge (1994)Google Scholar
  22. 22.
    Duch, W., Adamczak, R., Grabczewski, K.: A new methodology of extraction, optimisation and application of crisp and fuzzy logical rules. IEEE Transactions on Neural Networks 11(2) (2000)Google Scholar
  23. 23.
    Martinez, A., Goddard, J.: Definición de una red neuronal para clasificación por medio de un programa evolutivo. Mexican Journal of Biomedical Engineering 22, 4–11 (2001)Google Scholar
  24. 24.
    Rabuñal, J.R.: Entrenamiento de redes de neuronas artificiales mediante algoritmos genéticos. Graduate Thesis , University of A Coruña, Spain (1999)Google Scholar
  25. 25.
    Rivero, D., Dorado, J., Rabuñal, J., Pazos, A.: Using Genetic Programmning for Artificial Neural Network Development and Simplification. In: Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics (CIMMACS’06), pp. 65–71. WSEAS Press (2006)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Daniel Rivero
    • 1
  • Juan Rabuñal
    • 1
  • Julián Dorado
    • 1
  • Alejandro Pazos
    • 1
  1. 1.Department of Information & Communications Technologies, Campus Elviña, 15071, A CoruñaSpain

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