Fuzzy Random Forest with C–Fuzzy Decision Trees

  • Łukasz GadomerEmail author
  • Zenon A. Sosnowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9842)


In this paper a new classification solution which joins C–Fuzzy Decision Trees and Fuzzy Random Forest is proposed. Its assumptions are similar to the Fuzzy Random Forest, but instead of fuzzy trees it consists of C–Fuzzy Decision Trees. To test the proposed classifier there was performed a set of experiments. These experiments were performed using four datasets: Ionosphere, Dermatology, Pima–Diabetes and Hepatitis. Created forest was compared to C4.5 rev. 8 Decision Tree and single C–Fuzzy Decision Tree. The influence of randomness on the classification accuracy was also tested.


Fuzzy tree C–Fuzzy Decision Tree Fuzzy random forest 



This work was supported by the grant S/WI/1/2013 from Bialystok University of Technology founded by Ministry of Science and Higher Education.


  1. 1.
    Bonissone, P.P., Cadenas, J.M., Garrido, M.C., Diaz-valladares, R.A.: A fuzzy random forest: fundamental for design and construction. In: Proceedings of the 12th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2008), pp. 1231–1238 (2008)Google Scholar
  2. 2.
    Bonissone, P.P., Cadenas, J.M., Garrido, M.C., Diaz-Valladares, R.A.: A fuzzy random forest. Int. J. Approximate Reasoning 51(7), 729–747 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Bonissone, P.P., Cadenas, J.M., Garrido, M.C., Diaz-Valladares, R.A.: Combination methods in a fuzzy random forest. In: IEEE International Conference on Systems, Man and Cybernetics, SMC 2008, pp. 1794–799, October 2008Google Scholar
  4. 4.
    Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth and Brooks, Monterey (1984)zbMATHGoogle Scholar
  5. 5.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)zbMATHCrossRefGoogle Scholar
  6. 6.
    Chang, R.L.P., Pavlidis, T.: Fuzzy decision tree algorithms. IEEE Trans. Syst. Man Cybern. 7(1), 28–35 (1977)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Janikow, C.Z.: Fuzzy decision trees: issues and methods. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 28(1), 1–14 (1998)CrossRefGoogle Scholar
  8. 8.
    Lichman, M.: UCI machine learning repository (2013)Google Scholar
  9. 9.
    Pedrycz, W., Sosnowski, Z.A.: C-fuzzy decision trees. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 35(4), 498–511 (2005)CrossRefGoogle Scholar
  10. 10.
    Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)Google Scholar
  11. 11.
    Yuan, Y., Shaw, M.J.: Induction of fuzzy decision trees. Fuzzy Sets Syst. 69(2), 125–139 (1995)MathSciNetCrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (, which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  1. 1.Faculty of Computer ScienceBialystok University of TechnologyBialystokPoland

Personalised recommendations