Skip to main content
SpringerLink
Log in

Exploration of Bagging Ensembles Comprising Genetic Fuzzy Models to Assist with Real Estate Appraisals

  • Conference paper
Intelligent Data Engineering and Automated Learning - IDEAL 2009 (IDEAL 2009)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5788))

Abstract

The study reported was devoted to investigate to what extent bagging approach could lead to the improvement of the accuracy machine learning regression models. Four algorithms implemented in the KEEL tool, including two evolutionary fuzzy systems, decision trees for regression, and neural network, were used in the experiments. The results showed that some bagging ensembles ensured higher prediction accuracy than single models.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alcalá-Fdez, J., et al.: KEEL: A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems. Soft Computing 13(3), 307–318 (2009)

    Article  Google Scholar 

  2. Banfield, R.E., et al.: A Comparison of Decision Tree Ensemble Creation Techniques. IEEE Trans. on Pattern Analysis and Machine Intelligence 29(1), 173–180 (2007)

    Article  Google Scholar 

  3. Breiman, L.: Bagging Predictors. Machine Learning 24(2), 123–140 (1996)

    MATH  Google Scholar 

  4. Büchlmann, P., Yu, B.: Analyzing Bagging. Annals of Statistics 30, 927–961 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  5. Canul-Reich, J., Shoemaker, L., Hall, L.O.: Ensembles of Fuzzy Classifiers. In: Proc. IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2007, pp. 1–6 (2007)

    Google Scholar 

  6. Casillas, J., Cordón, O., Herrera, F.: COR: A Methodology to Improve ad hoc Data-Driven Linguistic Rule Learning Methods by Inducing Cooperation Among Rules. IEEE Trans. on System, Man and Cybernetics, Part B: Cybernetics 32(4), 526–537 (2002)

    Article  Google Scholar 

  7. Chen, T., Ren, J.: Bagging for Gaussian Process Regression. Neurocomputing 72(7-9), 1605–1610 (2009)

    Article  Google Scholar 

  8. Cordón, O., Herrera, F.: A Three-stage Evolutionary Process for Learning Descriptive and Approximate Fuzzy Logic Controller Knowledge Bases from Examples. International Journal of Approximate Reasoning 17(4), 369–407 (1997)

    Article  MATH  Google Scholar 

  9. Islam, M.M., et al.: Bagging and Boosting Negatively Correlated Neural Networks. IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics 38(3), 771–784 (2008)

    Article  Google Scholar 

  10. Kégl, B.: Robust Regression by Boosting the Median. In: Proceedings of the 16th Conference on Computational Learning Theory, pp. 258–272 (2003)

    Google Scholar 

  11. Kim, D.: Improving the Fuzzy System Performance by Fuzzy System Ensemble. Fuzzy Sets and Systems 98(1), 43–56 (1998)

    Article  Google Scholar 

  12. Korytkowski, M., Rutkowski, L., Scherer, R.: From Ensemble of Fuzzy Classifiers to Single Fuzzy Rule Base Classifier. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 265–272. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  13. Król, D., Lasota, T., Trawiński, B., Trawiński, K.: Investigation of Evolutionary Optimization Methods of TSK Fuzzy Model for Real Estate Appraisal. International Journal of Hybrid Intelligent Systems 5(3), 111–128 (2008)

    Article  MATH  Google Scholar 

  14. Lasota, T., Mazurkiewicz, J., Trawiński, B., Trawiński, K.: Comparison of Data Driven Models for the Validation of Residential Premises Using KEEL. International Journal of Hybrid Intelligent Systems (in press, 2009)

    Google Scholar 

  15. Moller, F.: A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning. Neural Networks 6, 525–533 (1990)

    Article  Google Scholar 

  16. Polikar, R.: Ensemble Learning. Scholarpedia 4(1), 27–76 (2009)

    Article  Google Scholar 

  17. Quinlan, J.R.: Learning with Continuous Classes. In: 5th Australian Joint Conference on Artificial Intelligence (AI 1992), Singapore, pp. 343–348 (1992)

    Google Scholar 

  18. Schapire, R.E.: The Strength of Weak Learnability. Mach. Learning 5(2), 197–227 (1990)

    Google Scholar 

  19. Wang, I., Witten, I.H.: Induction of Model Trees for Predicting Continuous Classes. In: van Someren, M., Widmer, G. (eds.) ECML 1997. LNCS, vol. 1224, pp. 128–137. Springer, Heidelberg (1997)

    Google Scholar 

  20. Wang, L.X., Mendel, J.M.: Generating Fuzzy Rules by Learning from Examples. IEEE Trans. on Systems, Man and Cybernetics 22(6), 1414–1427 (1992)

    Article  MathSciNet  Google Scholar 

  21. Wolpert, D.H.: Stacked Generalization. Neural Networks 5(2), 241–259 (1992)

    Article  Google Scholar 

  22. Yan, J.: Ensemble SVM Regression Based Multi-View Face Detection System. In: IEEE Workshop on Machine Learning for Signal Processing, pp. 163–169 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Spatial Management, Wrocław University of Environmental and Life Sciences, Ul. Norwida 25/27, 50-375, Wroclaw, Poland

    Tadeusz Lasota

  2. Institute of Informatics, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland

    Zbigniew Telec & Bogdan Trawiński

  3. European Centre for Soft Computing, Edificio Científico-Tecnológico, 3a Planta, C. Gonzalo Gutiérrez Quirós S/N, 33600, Mieres, Asturias, Spain

    Krzysztof Trawiński

Authors
  1. Tadeusz Lasota
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zbigniew Telec
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bogdan Trawiński
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Krzysztof Trawiński
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Escuela Politécnica Superior, Universidad de Burgos, Calle Francisco de Vitoria, S/N, Edifico C, 09006, Burgos, Spain

    Emilio Corchado

  2. School of Electrical and Electronic Engineering, University of Manchester, Sackville Street Building, Sackville Street, M60 1QD, Manchester, UK

    Hujun Yin

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lasota, T., Telec, Z., Trawiński, B., Trawiński, K. (2009). Exploration of Bagging Ensembles Comprising Genetic Fuzzy Models to Assist with Real Estate Appraisals. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_67

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04394-9_67

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04393-2

  • Online ISBN: 978-3-642-04394-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation