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Comparative Analysis of Premises Valuation Models Using KEEL, RapidMiner, and WEKA

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 5796)

Abstract

The experiments aimed to compare machine learning algorithms to create models for the valuation of residential premises, implemented in popular data mining systems KEEL, RapidMiner and WEKA, were carried out. Six common methods comprising two neural network algorithms, two decision trees for regression, and linear regression and support vector machine were applied to actual data sets derived from the cadastral system and the registry of real estate transactions. A dozen of commonly used performance measures was applied to evaluate models built by respective algorithms. Some differences between models were observed.

Keywords

  • machine learning
  • property valuation
  • KEEL
  • RapidMiner
  • WEKA

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  • DOI: 10.1007/978-3-642-04441-0_70
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Graczyk, M., Lasota, T., Trawiński, B. (2009). Comparative Analysis of Premises Valuation Models Using KEEL, RapidMiner, and WEKA. In: Nguyen, N.T., Kowalczyk, R., Chen, SM. (eds) Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems. ICCCI 2009. Lecture Notes in Computer Science(), vol 5796. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04441-0_70

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  • DOI: https://doi.org/10.1007/978-3-642-04441-0_70

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04440-3

  • Online ISBN: 978-3-642-04441-0

  • eBook Packages: Computer ScienceComputer Science (R0)