Dynamic Successive Feed-Forward Neural Network for Learning Fuzzy Decision Tree

  • Manu Pratap Singh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6743)


Fuzzy decision trees have been substantiated to be a valuable tool and more efficient than neural networks for pattern recognition task due to some facts like computation in making decisions are simpler and important features can be selected automatically during the design process. Here we present a feed forward neural network which learns fuzzy decision trees during the descent along the branches for its classification. Every decision instances of decision tree are represented by a node in neural network. The neural network provides the degree of membership of each possible move to the fuzzy set < < good move > > corresponding to each decision instance. These fuzzy values constitute the core of the probability of selecting the move out of the set of the children of the current node. This results in a natural way for driving the sharp discrete-state process running along the decision tree by means of incremental methods on the continuous-valued parameters of the neural network. A simulation program in C has been deliberated and developed for analyzing the consequences. The effectiveness of the learning process is tested through experiments with three real-world classification problems.


Decision tree pattern classification fuzzy system artificial neural networks fuzzy logic 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)Google Scholar
  2. 2.
    Zadeh, L.A.: Fuzzy Sets. Information and Control 8(3), 338–353 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Schalkoff, R.: Pattern Recognition: Statistical, Structural and Neural Appraoches. John Wiley & Sons, New Work (1992)Google Scholar
  4. 4.
    Olaru, C., Wehenkel, L.: A Complete Fuzzy Decision Tree Technique. Fuzzy Sets and Systems 138, 221–254 (2003)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Valiant, L.: A theory of the learnable. Communication of ACM 27, 1134–1142 (1984)CrossRefzbMATHGoogle Scholar
  6. 6.
    Kushilevitz, E., Mansour, Y.: Learning decision trees using the Fourier spectrum. Siam Journal of Computer Science 22(6), 1331–1348 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Hancock, T.: Learning 2m DNF and km decision trees. In: 4th COLT, pp. 199–308 (1991)Google Scholar
  8. 8.
    Bellare, M.: A technique for upper bounding the spectral norm with application to learning. In: 5th Annual Workshop on Computational Learning Theory, pp. 62–70 (1992)Google Scholar
  9. 9.
    Sakay, Y., Takimoto, E., Maruoka, A.: Proper learning algorithm for functions of k-terms under smooth distributions. In: Proc. of the 8th Workshop on Computational Learning Theory, pp. 206–213. Morgan Kaufmann, San Francisco (1995)Google Scholar
  10. 10.
    Erenfeucht, A., Haussler, D.: Learning decision trees from random examples. Inform. and Comp. 82(3), 231–246 (1989)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Hopfield, J., Tank, D.: Neural computations of decisions in optimization problems. Biological Cybernetics 52(3), 141–152 (1985)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Saylor, J., Stork, D.: Parallel analog neural networks for tree searching. In: Proc. Neural Networks for Computing, pp. 392–397 (1986)Google Scholar
  13. 13.
    Szczerbicki, E.: Decision trees and neural networks for reasoning and knowledge acquisition for autonomous agents. International Journal of Systems Science 27(2), 233–239 (1996)CrossRefzbMATHGoogle Scholar
  14. 14.
    Sethi, I.: Entropy nets: from decision trees to neural networks. Proceedings of the IEEE 78, 1605–1613 (1990)CrossRefGoogle Scholar
  15. 15.
    Ivanova, I., Kubat, M.: Initialization of neural networks by means of decision trees. Knowledge-Based systems 8(6), 333–344 (1995)CrossRefGoogle Scholar
  16. 16.
    Geurts, P., Wehenkel, L.: Investigation and reduction of discretization variance in decision tree induction. In: Lopez de Mantaras, R., Plaza, E. (eds.) ECML 2000. LNCS (LNAI), vol. 1810, pp. 162–170. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  17. 17.
    Anderson, E.: The Irises of the Gaspe peninsula, Bulletin America, IRIS Soc. (1935)Google Scholar
  18. 18.
    Budihardjo, A., Grzymala-Busse, J., Woolery, L.: Program LERS_LB 2.5 as a tool for knowledge acquisition in nursing. In: Proceedings of the 4th Int. Conference on Industrial & Engineering Applications of AI & Expert Systems, pp. 735–740 (1991)Google Scholar
  19. 19.
    Jain, M., Butey, P.K., Singh, M.P.: Classification of Fuzzy-Based Information using Improved backpropagation algorithm of Artificial Neural Networks. International Journal of Computational Intelligence Research 3(3), 265–273 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Manu Pratap Singh
    • 1
  1. 1.Department of Computer ScienceICIS, Dr. B. R. Ambedkar UniversityAgraIndia

Personalised recommendations