Skip to main content
SpringerLink
Log in
Book cover

Asian Conference on Intelligent Information and Database Systems

ACIIDS 2010: Intelligent Information and Database Systems pp 330–339Cite as

Analysis of Bagging Ensembles of Fuzzy Models for Premises Valuation

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5991))

Abstract

The investigation of 16 fuzzy algorithms implemented in data mining system KEEL from the point of view of their usefulness to create bagging ensemble models to assist with real estate appraisal were presented in the paper. All the experiments were conducted with a real-world dataset derived from a cadastral system and registry of real estate transactions. The results showed there were significant differences in accuracy between individual algorithms. The analysis of measures of error diversity revealed that only the highest values of an average pairwise correlation of outputs were a profitable criterion for the selection of ensemble members.

This is a preview of subscription content, 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aksela, M., Laaksonen, J.: Using diversity of errors for selecting members of a committee classifier. Pattern Recognition 39, 608–623 (2006)

    Article  MATH  Google Scholar 

  2. 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 

  3. 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 

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

    MATH  MathSciNet  Google Scholar 

  5. Brown, G., Wyatt, J., Harris, R., Yao, X.: Diversity Creation Methods: A Survey and Categorisation. Journal of Information Fusion 6(1), 5–20 (2005)

    Article  Google Scholar 

  6. 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 

  7. Chandra, A., Yao, X.: Evolving hybrid ensembles of learning machines for better generalisation. Neurocomputing 69, 686–700 (2006)

    Article  Google Scholar 

  8. Chawla, N.V., Hall, L.O., Bowyer, K.W., Kegelmeyer, W.P.: Learning Ensembles From Bites: A Scalable and Accurate Approach. Journal of Machine Learning Research 5, 421–451 (2004)

    MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  10. Cordón, O., Quirin, A.: Comparing Two Genetic Overproduce-and-choose Strategies for Fuzzy Rule-based Multiclassification Systems Generated by Bagging and Mutual Information-based Feature Selection. Int. J. Hybrid Intelligent Systems (2009) (in press)

    Google Scholar 

  11. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)

    Google Scholar 

  12. Dos Santos, E.M., Sabourin, R., Maupin, P.: A dynamic overproduce-and-choose strategy for the selection of classifier ensembles. Pattern Recognition 41(10), 2993–3009 (2008)

    Article  MATH  Google Scholar 

  13. Freund, Y., Schapire, R.E.: Decision-theoretic generalization of on-line learning and an application to boosting. J. Computer and System Sciences 55(1), 119–139 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  14. García-Pedrajas, N.: Constructing Ensembles of Classifiers by Means of Weighted Instance Selection. IEEE Transactions on Neural Networks 20(2), 258–277 (2009)

    Article  Google Scholar 

  15. Hernandez-Lobato, D., Martinez-Munoz, G., Suarez, A.: Pruning in ordered regression bagging ensembles. In: Yen, G.G. (ed.) Proceedings of the IEEE World Congress on Computational Intelligence, pp. 1266–1273 (2006)

    Google Scholar 

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

    Article  Google Scholar 

  17. Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixtures of local experts. Neural Computation 3, 79–87 (1991)

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. 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)

    MATH  Google Scholar 

  20. Krzystanek, M., Lasota, T., Trawiński, B.: Comparative Analysis of Evolutionary Fuzzy Models for Premises Valuation Using KEEL. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS, vol. 5796, pp. 838–849. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  21. Kuncheva, L.I., Whitaker, C.J.: Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy. Machine Learning 51, 181–207 (2003)

    Article  MATH  Google Scholar 

  22. 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 (2009) (in press)

    Google Scholar 

  23. Lasota, T., Telec, Z., Trawiński, B., Trawiński, K.: A Multi-agent System to Assist with Real Estate Appraisals using Bagging Ensembles. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS, vol. 5796, pp. 813–824. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  24. Lasota, T., Telec, Z., Trawiński, B., Trawiński, K.: Exploration of Bagging Ensembles Comprising Genetic Fuzzy Models to Assist with Real Estate Appraisals. In: Corchado, E., Yin, H. (eds.) IDEAL 2009. LNCS, vol. 5788, pp. 554–561. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  25. Liu, Y., Yao, X.: Ensemble learning via negative correlation. Neural Networks 12(10), 1399–1404 (1999)

    Article  Google Scholar 

  26. Margineantu, D.D., Dietterich, T.G.: Pruning Adaptive Boosting. In: Proc. 14th Int. Conf. Machine Learning, pp. 211–218 (1997)

    Google Scholar 

  27. Polikar, R.: Ensemble Learning. Scholarpedia 4(1), 2776 (2009)

    Article  Google Scholar 

  28. Rokach, L.: Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography. Comp. Stat. and Data Analysis 53, 4046–4072 (2009)

    Article  MATH  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  31. Zhou, Z.H., Wu, J., Tang, W.: Ensembling Neural Networks: Many Could Be Better Than All. Artificial Intelligence 137, 239–263 (2002)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Marek Krzystanek, Zbigniew Telec & Bogdan Trawiński

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

    Tadeusz Lasota

Authors
  1. Marek Krzystanek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tadeusz Lasota
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zbigniew Telec
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bogdan Trawiński
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Informatics, Wroclaw University of Technology, Str. Wyb. Wyspianskiego 27, 50-370, Poland

    Ngoc Thanh Nguyen

  2. Hue University, Str. Le Loi 3, Hue City, Vietnam

    Manh Thanh Le

  3. Faculty of Computer Science and Management, Wroclaw University of Technology, Str. Lukasiewicza, 50-370, Wroclaw, Poland

    Jerzy Świątek

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Krzystanek, M., Lasota, T., Telec, Z., Trawiński, B. (2010). Analysis of Bagging Ensembles of Fuzzy Models for Premises Valuation. In: Nguyen, N.T., Le, M.T., Świątek, J. (eds) Intelligent Information and Database Systems. ACIIDS 2010. Lecture Notes in Computer Science(), vol 5991. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12101-2_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-12101-2_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12100-5

  • Online ISBN: 978-3-642-12101-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation