Investigation of Evolving Fuzzy Systems Methods FLEXFIS and eTS on Predicting Residential Prices

  • Bogdan Trawiński
  • Krzysztof Trawiński
  • Edwin Lughofer
  • Tadeusz Lasota
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6857)

Abstract

In this paper, we investigate on-line fuzzy modeling for predicting the prices of residential premises using the concept of evolving fuzzy models. These combine the aspects of incrementally updating the parameters and expanding the inner structure on demand with the concepts of uncertainty modeling in a possibilistic and linguistic manner (via fuzzy sets and fuzzy rule bases). The FLEXFIS and eTS approaches are evolving fuzzy models used to compare with an expert-based property valuating method as well as with a classic genetic fuzzy system. We use a real-world dataset taken from a cadastral system for that comparison.

Keywords

Root Mean Square Error Real Estate Geographic Information System Fuzzy System Fuzzy Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alonso, J.M., Magdalena, L., González-Rodríguez, G.: Looking for a good fuzzy system interpretability index: An experimental approach. Int. J. of Approximate Reasoning 51, 115–134 (2009)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Angelov, P., Filev, D.: An approach to online identification of Takagi-Sugeno fuzzy models. IEEE Trans. Syst., Man, Cybern. B 34(1), 484–498 (2004)CrossRefGoogle Scholar
  3. 3.
    Angelov, P., Lughofer, E.: Data-driven evolving fuzzy systems using eTS and FLEXFIS: Comparative analysis. Int. J. of General Systems 37(1), 45–67 (2008)CrossRefMATHGoogle Scholar
  4. 4.
    Bonissone, P., Cheetham, W.: Financial applications of fuzzy case-based reasoning to residential property valuation. In: Proceedings of the Sixth IEEE Int. Conference on Fuzzy Systems 1997, vol. 1, pp. 37–44 (July 1997)Google Scholar
  5. 5.
    Castro, J., Delgado, M.: Fuzzy systems with defuzzification are universal approximators. IEEE Trans. Syst., Man, Cybern. B 26, 149–152 (1996)CrossRefGoogle Scholar
  6. 6.
    D’Amato, M.: Comparing rough set theory with multiple regression analysis as automated valuation methodologies. Int. Real Estate Review 10(2), 42–65 (2007)Google Scholar
  7. 7.
    Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. of Machine Learning Research 7, 1–30 (2006)MathSciNetMATHGoogle Scholar
  8. 8.
    García, N., Gámez, M., Alfaro, E.: ANN+GIS: An automated system for property valuation. Neurocomputing 71(4–6), 733–742 (2008)CrossRefGoogle Scholar
  9. 9.
    García, S., Herrera, F.: An extension on statistical comparisons of classifiers over multiple data sets. J. of Machine Learning Research 9, 2677–2694 (2008)MATHGoogle Scholar
  10. 10.
    González, M.A.S., Formoso, C.: Mass appraisal with genetic fuzzy rule-based systems. Property Management 24(1), 20–30 (2006)CrossRefGoogle Scholar
  11. 11.
    Guan, J., Zurada, J., Levitan, A.S.: An adaptive neuro-fuzzy inference system based approach to real estate property assessment. J. of Real Estate Research 30(4), 395–422 (2008)Google Scholar
  12. 12.
    Kempa, O., Lasota, T., Telec, Z., Trawiński, B.: Investigation of bagging ensembles of genetic neural networks and fuzzy systems for real estate appraisal. In: Nguyen, N.T., Kim, C.-G., Janiak, A. (eds.) ACIIDS 2011, Part II. LNCS (LNAI), vol. 6592, pp. 323–332. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  13. 13.
    Kontrimas, V., Verikas, A.: The mass appraisal of the real estate by computational intelligence. Applied Soft Computing 11(1), 443–448 (2011)CrossRefGoogle Scholar
  14. 14.
    Kosko, B.: Fuzzy systems as universal approximators. IEEE Trans. on Computers 43(11), 1329–1333 (1994)CrossRefMATHGoogle Scholar
  15. 15.
    Król, D., Lasota, T., Trawiński, B., Trawiński, K.: Investigation of evolutionary optimization methods of tsk fuzzy model for real estate appraisal. Int. J. Hybrid Intell. Syst. 5(3), 111–128 (2008)CrossRefMATHGoogle Scholar
  16. 16.
    Lasota, T., Telec, Z., Trawiński, B., Trawiński, K.: Investigation of the ets evolving fuzzy systems applied to real estate appraisal. J. of Multiple-Valued Logic and Soft Computing 17(2-3), 229–253 (2011)Google Scholar
  17. 17.
    Lughofer, E.: FLEXFIS: A robust incremental learning approach for evolving TS fuzzy models. IEEE Trans. on Fuzzy Systems 16(6), 1393–1410 (2008)CrossRefGoogle Scholar
  18. 18.
    Lughofer, E., Bouchot, J.L.: On-line elimination of local redundancies in evolving fuzzy systems, evolving systems. Evolving Systems (2011) (in revision)Google Scholar
  19. 19.
    Lughofer, E., Klement, E.: FLEXFIS: A variant for incremental learning of Takagi-Sugeno fuzzy systems. In: Proceedings of FUZZ-IEEE 2005, Reno, Nevada, U.S.A, pp. 915–920 (2005)Google Scholar
  20. 20.
    Nguyen, N., Cripps, A.: Predicting housing value: A comparison of multiple regression analysis and artificial neural networks. J. of Real Estate Research 22(3), 313–336 (2001)Google Scholar
  21. 21.
    Selim, H.: Determinants of house prices in turkey: Hedonic regression versus artificial neural network. Expert Systems with Applications 36, 2843–2852 (2009)CrossRefGoogle Scholar
  22. 22.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst., Man, Cybern. 15(1), 116–132 (1985)CrossRefMATHGoogle Scholar
  23. 23.
    Wang, L., Mendel, J.: Fuzzy basis functions, universal approximation and orthogonal least-squares learning. IEEE Trans. Neural Networks 3(5), 807–814 (1992)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Bogdan Trawiński
    • 1
  • Krzysztof Trawiński
    • 2
  • Edwin Lughofer
    • 3
  • Tadeusz Lasota
    • 4
  1. 1.Institute of InformaticsWrocław University of TechnologyWrocławPoland
  2. 2.European Centre for Soft ComputingEdificio Científico-TecnológicoMieresSpain
  3. 3.Department of Knowledge-based Mathematical SystemsJohannes Kepler University LinzLinzAustria
  4. 4.Dept. of Spatial ManagementWrocław University of Environmental and Life SciencesWrocławPoland

Personalised recommendations