Skip to main content

Experimental Comparison of Resampling Methods in a Multi-Agent System to Assist with Property Valuation

  • Conference paper
Agent and Multi-Agent Systems: Technologies and Applications (KES-AMSTA 2011)

Abstract

A new version of the a multi-agent system to aid in real estate appraisal, called MAREA-2, was introduced. The system is being developed using Java Spring Framework and is intended for industrial application in cadastral information centres. The major part of the study was devoted to investigate the performance of Bagging, Subagging, and Repeated cross-validation models. The overall result of our investigation was that the majority of models created using resampling techniques provided better or equivalent accuracy than the experts’ method employed in reality. It confirms that automated valuation models can be successfully incorporated into the multi-agent system and be utilized to support appraisers’ work.

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

Access this chapter

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. Bańczyk, K.: Multi-agent system based on heterogeneous ensemble machine learning models. Master’s Thesis, Wrocław University of Technology, Wrocław, Poland (2011)

    Google Scholar 

  2. Biau, G., Cérou, F., Guyader, A.: On the Rate of Convergence of the Bagged Nearest Neighbor Estimate. Journal of Machine Learning Research 11, 687–712 (2010)

    MathSciNet  MATH  Google Scholar 

  3. Borra, S., Di Ciaccio, A.: Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods. Computational Statistics & Data Analysis 54(12), 2976–2989 (2010)

    Article  MathSciNet  MATH  Google Scholar 

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

    MATH  Google Scholar 

  5. Bühlmann, P., Yu, B.: Analyzing bagging. Annals of Statistics 30, 927–961 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  6. Burman, P.: A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods. Biometrika 76, 503–514 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  7. Friedman, J.H., Hall, P.: On bagging and nonlinear estimation. Journal of Statistical Planning and Inference 137(3), 669–683 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  8. Fumera, G., Roli, F., Serrau, A.: A theoretical analysis of bagging as a linear combination of classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(7), 1293–1299 (2008)

    Article  Google Scholar 

  9. Graczyk, M., Lasota, T., Telec, Z., Trawiński, B.: A Multi-Agent System to Assist with Property Valuation Using Heterogeneous Ensembles of Fuzzy Models. In: Jędrzejowicz, P., Nguyen, N.T., Howlet, R.J., Jain, L.C. (eds.) KES-AMSTA 2010. LNCS, vol. 6070, pp. 420–429. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  10. Hohpe, G., Woolf, B.: Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions. Addison-Wesley, Boston (2004)

    Google Scholar 

  11. Lasota, T., Telec, Z., Trawiński, B., Trawiński, K.: Concept of a Multi-Agent System for Assisting in Real Estate Appraisals. In: Håkansson, A., Nguyen, N.T., Hartung, R.L., Howlett, R.J., Jain, L.C. (eds.) KES-AMSTA 2009. LNCS, vol. 5559, pp. 50–59. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  12. 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 

  13. Molinaro, A.N., Simon, R., Pfeiffer, R.M.: Prediction error estimation: a comparison of resampling methods. Bioinformatics 21(15), 3301–3307 (2005)

    Article  Google Scholar 

  14. Rodriguez, J.D., Perez, A., Lozano, J.A.: Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(3), 569–575 (2010)

    Article  Google Scholar 

  15. Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lasota, T., Łuczak, T., Trawiński, B. (2011). Experimental Comparison of Resampling Methods in a Multi-Agent System to Assist with Property Valuation. In: O’Shea, J., Nguyen, N.T., Crockett, K., Howlett, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2011. Lecture Notes in Computer Science(), vol 6682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22000-5_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-22000-5_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21999-3

  • Online ISBN: 978-3-642-22000-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics