Skip to main content
SpringerLink
Log in

Investigation of Incremental Support Vector Regression Applied to Real Estate Appraisal

  • Conference paper
Intelligent Information and Database Systems (ACIIDS 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7803))

Included in the following conference series:

  • 2182 Accesses

Abstract

Incremental support vector regression algorithms (SVR) and sequential minimal optimization algorithms (SMO) for regression were implemented. Intensive experiments to compare predictive accuracy of the algorithms with different kernel functions over several datasets taken from a cadastral system were conducted in offline scenario. The statistical analysis of experimental output was made employing the nonparametric methodology designed especially for multiple N×N comparisons of N algorithms over N datasets including Friedman tests followed by Nemenyi’s, Holm’s, Shaffer’s, and Bergmann-Hommel’s post-hoc procedures. The results of experiments showed that most of SVR algorithms outperformed significantly a pairwise comparison method used by the experts to estimate the values of residential premises over all datasets. Moreover, no statistically significant differences between incremental SVR and non-incremental SMO algorithms were observed using our stationary cadastral datasets. The results open the opportunity of further research into the application of incremental SVR algorithms to predict from a data stream of real estate sales/purchase transactions.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Peterson, S., Flangan, A.B.: Neural Network Hedonic Pricing Models in Mass Real Estate Appraisal. Journal of Real Estate Research 31(2), 147–164 (2009)

    Google Scholar 

  2. Pi-ying, L.: Analysis of the Mass Appraisal Model by Using Artificial Neural Network in Kaohsiung City. Journal of Modern Accounting and Auditing 7(10), 1080–1089 (2011)

    Google Scholar 

  3. González, M.A.S., Formoso, C.T.: Mass appraisal with genetic fuzzy rule-based systems. Property Management 24(1), 20–30 (2006)

    Article  Google Scholar 

  4. Kusan, H., Aytekin, O., Özdemir, I.: The use of fuzzy logic in predicting house selling price. Expert Systems with Applications 37(3), 1808–1813 (2010)

    Article  Google Scholar 

  5. Kontrimas, V., Verikas, A.: The mass appraisal of the real estate by computational intelligence. Applied Soft Computing 11(1), 443–448 (2011)

    Article  Google Scholar 

  6. Zurada, J., Levitan, A.S., Guan, J.: A Comparison of Regression and Artificial Intelligence Methods in a Mass Appraisal Context. Journal of Real Estate Research 33(3), 349–388 (2011)

    Google Scholar 

  7. Graczyk, M., Lasota, T., Trawiński, B.: Comparative Analysis of Premises Valuation Models Using KEEL, RapidMiner, and WEKA. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS (LNAI), vol. 5796, pp. 800–812. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  8. 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 

  9. 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 7(1), 3–16 (2010)

    MATH  Google Scholar 

  10. Lasota, T., Telec, Z., Trawiński, B., Trawiński, K.: Investigation of the eTS Evolving Fuzzy Systems Applied to Real Estate Appraisal. Journal of Multiple-Valued Logic and Soft Computing 17(2-3), 229–253 (2011)

    Google Scholar 

  11. Lughofer, E., Trawiński, B., Trawiński, K., Kempa, O., Lasota, T.: On Employing Fuzzy Modeling Algorithms for the Valuation of Residential Premises. Information Sciences 181, 5123–5142 (2011)

    Article  Google Scholar 

  12. Basak, D., Pal, S., Patranabis, D.C.: Support Vector Regression. Neural Information Processing – Letters and Reviews 11(10), 203–224 (2007)

    Google Scholar 

  13. Smola, A.J., Schölkopf, B.: A Tutorial on Support Vector Regression. Statistics and Computing 14, 199–222 (2004)

    Article  MathSciNet  Google Scholar 

  14. Chang, C.C., Lin, C.J.: Training ν-support vector regression: Theory and algorithms. Neural Computation 14, 1959–1976 (2002)

    Article  MATH  Google Scholar 

  15. Ma, J., Thelier, J., Perkins, S.: Accurate on-line Support Vector Regression modeling. Neural Computation 15(11), 2683–2703 (2003)

    Article  MATH  Google Scholar 

  16. Gâlmeanu, H., Andonie, A.: Incremental / decremental SVM for function approximation. In: Proc. of the 11th International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2008 (2008), doi:10.1109/OPTIM.2008.4602473

    Google Scholar 

  17. Cauwenberghs, G., Poggio, T.: Incremental and Decremental Support Vector Machine Learning. In: Leen, T.K., et al. (eds.) Advances in Neural Information Processing Systems, vol. 13, pp. 409–415. MIT Press, Cambridge (2001)

    Google Scholar 

  18. Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)

    Google Scholar 

  19. Shevade, S.K., Keerthi, S.S., Bhattacharyya, C., Murthy, K.R.K.: Improvements to SMO Algorithm for SVM Regression. IEEE Transactions on Neural Networks 11(5), 1188–1193 (2000)

    Article  Google Scholar 

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

    MATH  Google Scholar 

  21. García, S., Herrera, F.: An Extension on “Statistical Comparisons of Classifiers over Multiple Data Sets” for all Pairwise Comparisons. Journal of Machine Learning Research 9, 2677–2694 (2008)

    MATH  Google Scholar 

  22. Trawiński, B., Smętek, M., Telec, Z., Lasota, T.: Nonparametric Statistical Analysis for Multiple Comparison of Machine Learning Regression Algorithms. International Journal of Applied Mathematics and Computer Science 22(4) (2012) (in print)

    Google Scholar 

  23. Patrascu, P.: Implementation and testing of incremental support vector regression system to assist with real estate appraisals. Master’s Thesis, Wrocław University of Technology, Wrocław, Poland (2011) (in Polish)

    Google Scholar 

  24. Staelin, C.: Parameter selection for support vector machines, HP Laboratories Israel. Tech. Rep. HPL-2002-354, R.1 (2002)

    Google Scholar 

  25. Momma, M., Bennett, K.P.: A Pattern Search Method for Model Selection of Support Vector Regression. In: SIAM Conference on Data Mining (2002)

    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, Wrocław, Poland

    Tadeusz Lasota

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

    Petru Patrascu, Bogdan Trawiński & Zbigniew Telec

Authors
  1. Tadeusz Lasota
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Petru Patrascu
    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. Zbigniew Telec
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Information Systems, Department of Software Engineering, Universiti Teknologi Malaysia, 81310, Johar Baharu, Johor, Malaysia

    Ali Selamat  & Habibollah Haron  & 

  2. Institute of Informatics, Division of Knowledge Managements Systems, Wrocław University of Technology, Str. Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland

    Ngoc Thanh Nguyen

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lasota, T., Patrascu, P., Trawiński, B., Telec, Z. (2013). Investigation of Incremental Support Vector Regression Applied to Real Estate Appraisal. In: Selamat, A., Nguyen, N.T., Haron, H. (eds) Intelligent Information and Database Systems. ACIIDS 2013. Lecture Notes in Computer Science(), vol 7803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36543-0_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-36543-0_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36542-3

  • Online ISBN: 978-3-642-36543-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation