A Multi-agent System to Assist with Real Estate Appraisals Using Bagging Ensembles

Authors:
Tadeusz Lasota;Zbigniew Telec;Bogdan Trawiński;Krzysztof Trawiński
Affiliations:
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;Institute of Informatics, Wrocław University of Technology, Wrocław, Poland 50-370;European Centre for Soft Computing, Edificio Científico-Tecnológico, Mieres, Spain 33600
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 multi-agent system for real estate appraisals MAREA was extended to include aggregating agents, which could create ensemble models applying the bagging approach, was presented in the paper. The major part of the study 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 linear regression, decision trees for regression, support vector machines, and artificial neural network of MLP type, were used in the experiments. The results showed that bagging ensembles ensured higher prediction accuracy than single models.