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

  • Authors:
  • Magdalena Graczyk;Tadeusz Lasota;Bogdan Trawiński

  • Affiliations:
  • Institute of Informatics, Wrocław University of Technology, Wrocław, Poland 50-370;Dept. of Spatial Management, Wrocław University of Environmental and Life Sciences, Wroclaw, Poland 50-375;Institute of Informatics, Wrocław University of Technology, Wrocław, Poland 50-370

  • Venue:
  • ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
  • Year:
  • 2009

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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.