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Exploration of Bagging Ensembles Comprising Genetic Fuzzy Models to Assist with Real Estate Appraisals

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Intelligent Data Engineering and Automated Learning - IDEAL 2009 (IDEAL 2009)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5788))

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Abstract

The study reported was devoted to investigate to what extent bagging approach could lead to the improvement of the accuracy machine learning regression models. Four algorithms implemented in the KEEL tool, including two evolutionary fuzzy systems, decision trees for regression, and neural network, were used in the experiments. The results showed that some bagging ensembles ensured higher prediction accuracy than single models.

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Author information

Authors and Affiliations

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

    Tadeusz Lasota

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

    Zbigniew Telec & Bogdan Trawiński

  3. European Centre for Soft Computing, Edificio Científico-Tecnológico, 3a Planta, C. Gonzalo Gutiérrez Quirós S/N, 33600, Mieres, Asturias, Spain

    Krzysztof Trawiński

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

Editors and Affiliations

  1. Escuela Politécnica Superior, Universidad de Burgos, Calle Francisco de Vitoria, S/N, Edifico C, 09006, Burgos, Spain

    Emilio Corchado

  2. School of Electrical and Electronic Engineering, University of Manchester, Sackville Street Building, Sackville Street, M60 1QD, Manchester, UK

    Hujun Yin

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© 2009 Springer-Verlag Berlin Heidelberg

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Lasota, T., Telec, Z., Trawiński, B., Trawiński, K. (2009). Exploration of Bagging Ensembles Comprising Genetic Fuzzy Models to Assist with Real Estate Appraisals. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_67

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  • DOI: https://doi.org/10.1007/978-3-642-04394-9_67

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04393-2

  • Online ISBN: 978-3-642-04394-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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