Comparison of bagging, boosting and stacking ensembles applied to real estate appraisal

Authors:
Magdalena Graczyk;Tadeusz Lasota;Bogdan Trawiński;Krzysztof Trawiński
Affiliations:
Wrocław University of Technology, Institute of Informatics, Wrocław, Poland;Wrocław University of Environmental and Life Sciences, Dept. of Spatial Management, Wrocław, Poland;Wrocław University of Technology, Institute of Informatics, Wrocław, Poland;European Centre for Soft Computing, Edificio Científico-Tecnológico, Mieres, Asturias, Spain
Venue:
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Year:
2010

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Abstract

The experiments, aimed to compare three methods to create ensemble models implemented in a popular data mining system called WEKA, were carried out. Six common algorithms comprising two neural network algorithms, two decision trees for regression, linear regression, and support vector machine were used to construct ensemble models. All algorithms were employed to real-world datasets derived from the cadastral system and the registry of real estate transactions. Nonparametric Wilcoxon signed-rank tests to evaluate the differences between ensembles and original models were conducted. The results obtained show there is no single algorithm which produces the best ensembles and it is worth to seek an optimal hybrid multi-model solution.