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An Attempt to Employ Genetic Fuzzy Systems to Predict from a Data Stream of Premises Transactions

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Scalable Uncertainty Management (SUM 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7520))

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Abstract

An approach to apply ensembles of genetic fuzzy systems, built over the chunks of a data stream, to aid in residential premises valuation was proposed. The approach consists in incremental expanding an ensemble by systematically generated models in the course of time. The output of aged component models produced for current data is updated according to a trend function reflecting the changes of premises prices since the moment of individual model generation. An experimental evaluation of the proposed method using real-world data taken from a dynamically changing real estate market revealed its advantage in terms of predictive accuracy.

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Author information

Authors and Affiliations

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

    Bogdan Trawiński & Magdalena Smętek

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

    Tadeusz Lasota

  3. Faculty of Electronics, Wrocław University of Technology, Wybrzeże S. Wyspiańskiego 27, 50-370, Wrocław, Poland

    Grzegorz Trawiński

Authors
  1. Bogdan Trawiński
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  2. Tadeusz Lasota
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  3. Magdalena Smętek
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  4. Grzegorz Trawiński
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, Marburg University, Hans-Meerwein-Strasse 6, 35032, Marburg, Germany

    Eyke Hüllermeier , Thomas Fober  & Bernhard Seeger ,  & 

  2. Department of Computer Science, Auckland University, 38 Princes St., 1010, Auckland, New Zealand

    Sebastian Link

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© 2012 Springer-Verlag Berlin Heidelberg

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Trawiński, B., Lasota, T., Smętek, M., Trawiński, G. (2012). An Attempt to Employ Genetic Fuzzy Systems to Predict from a Data Stream of Premises Transactions. In: Hüllermeier, E., Link, S., Fober, T., Seeger, B. (eds) Scalable Uncertainty Management. SUM 2012. Lecture Notes in Computer Science(), vol 7520. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33362-0_10

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  • DOI: https://doi.org/10.1007/978-3-642-33362-0_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33361-3

  • Online ISBN: 978-3-642-33362-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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