Comparison of Bagging, Boosting and Stacking Ensembles Applied to Real Estate Appraisal
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.
Keywordsensemble models bagging stacking boosting property valuation
Unable to display preview. Download preview PDF.
- 7.Cordón, O., Quirin, A.: Comparing Two Genetic Overproduce-and-choose Strategies for Fuzzy Rule-based Multiclassification Systems Generated by Bagging and Mutual Information-based Feature Selection. Int. J. Hybrid Intelligent Systems (2009) (in press)Google Scholar
- 8.Cunningham, S.J., Frank, E., Hall, M., Holmes, G., Trigg, L., Witten, I.H.: WEKA: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, New Zealand (2005)Google Scholar
- 13.Hernandez-Lobato, D., Martinez-Munoz, G., Suarez, A.: Pruning in ordered regression bagging ensembles. In: Yen, G.G. (ed.) Proceedings of the IEEE World Congress on Computational Intelligence, pp. 1266–1273 (2006)Google Scholar
- 15.Krogh, A., Vedelsby, J.: Neural network ensembles, cross validation, and active learning. In: Advances in Neural Inf. Proc. Systems, pp. 231–238. MIT Press, Cambridge (1995)Google Scholar
- 18.Lasota, T., Mazurkiewicz, J., Trawiński, B., Trawiński, K.: Comparison of Data Driven Models for the Validation of Residential Premises Using KEEL. International Journal of Hybrid Intelligent Systems (2009) (in press)Google Scholar
- 20.Margineantu, D.D., Dietterich, T.G.: Pruning Adaptive Boosting. In: Proc. 14th Int. Conf. Machine Learning, pp. 211–218 (1997)Google Scholar
- 23.Prodromidis, A.L., Chan, P.K., Stolfo, S.J.: Meta-Learning in a Distributed Data Mining System: Issues and Approaches. In: Kargupta, H., Chan, P.K. (eds.) Advances of Distributed Data Mining. AAAI Press, Menlo Park (2000)Google Scholar
- 25.Schapire, R.E.: The Strength of Weak Learnability. Mach. Learning 5(2), 197–227 (1990)Google Scholar