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Application of Mixture of Experts to Construct Real Estate Appraisal Models

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Hybrid Artificial Intelligence Systems (HAIS 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6076))

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Abstract

Several experiments were conducted in order to investigate the usefulness of mixture of experts (ME) approach to an online internet system assisting in real estate appraisal. All experiments were performed using 28 real-world datasets composed of data taken from a cadastral system and GIS data derived from a cadastral map. The analysis of the results was performed using recently proposed statistical methodology including nonparametric tests followed by post-hoc procedures designed especially for multiple 1×n and n×n comparisons. GLM (general linear model) architectures of mixture of experts achieved better results for ME with an adaptive variance parameter for each expert, whereas MLP (multilayer perceptron) architectures - for standard mixtures of experts.

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Author information

Authors and Affiliations

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

    Magdalena Graczyk, Zbigniew Telec & Bogdan Trawiński

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

    Tadeusz Lasota

Authors
  1. Magdalena Graczyk
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  2. Tadeusz Lasota
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  3. Zbigniew Telec
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  4. Bogdan Trawiński
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Editor information

Editors and Affiliations

  1. Facultad de informatica UPV/EHU, San Sebastian, Spain

    Manuel Graña Romay  & M. Teresa Garcia Sebastian  & 

  2. Universidad de Salamanca, Spain

    Emilio Corchado

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Graczyk, M., Lasota, T., Telec, Z., Trawiński, B. (2010). Application of Mixture of Experts to Construct Real Estate Appraisal Models. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_71

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  • DOI: https://doi.org/10.1007/978-3-642-13769-3_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13768-6

  • Online ISBN: 978-3-642-13769-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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