Evolving fuzzy systems based on the eTS learning algorithm for the valuation of residential premises

Authors:
Tadeusz Lasota;Zbigniew Telec;Bogdan Trawiński;Krzysztof Trawiński
Affiliations:
Wrocław University of Environmental and Life Sciences, Dept. of Spatial Management, Wroclaw, Poland;Wrocław University of Technology, Institute of Informatics, Wrocław, Poland;Wrocław University of Technology, Institute of Informatics, Wrocław, Poland;European Centre for Soft Computing, Mieres, Asturias, Spain
Venue:
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Year:
2009

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Abstract

An attempt has been made to employ evolving Takagi-Sugeno algorithm (eTS) to built models assisting property valuation on the basis of actual data drawn from cadastral system, registry of sales transactions, and a cadastral map. Seven methods of feature selection were applied an evaluated. The eTS performance was compared to three algorithms implemented in KEEL, including decision trees for regression, neural network, and support vector machine. The results confirmed the advantages of the eTS algorithm.