Abstract
A few years ago a new classifier ensemble method, called rotation forest, was devised. The technique applies Principal Component Analysis to rotate the original feature axes in order to obtain different training sets for learning base classifiers. In the paper we report the results of the investigation aimed to compare the predictive performance of rotation forest with random forest models, bagging ensembles and single models using two popular algorithms M5 tree and multilayer perceptron. All tests were carried out in the WEKA data mining system within the framework of 10-fold cross-validation and repeated holdout splits. A real-world dataset of sales/purchase transactions derived from a cadastral system served as basis for benchmarking the methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Breiman, L.: Bagging Predictors. Machine Learning 24(2), 123–140 (1996)
Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)
Bryll, R.: Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets. Pattern Recognition 20(6), 1291–1302 (2003)
Bühlmann, P., Yu, B.: Analyzing bagging. Annals of Statistics 30, 927–961 (2002)
Friedman, J.H., Hall, P.: On bagging and nonlinear estimation. Journal of Statistical Planning and Inference 137(3), 669–683 (2007)
Fumera, G., Roli, F., Serrau, A.: A theoretical analysis of bagging as a linear combination of classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 30(7), 1293–1299 (2008)
Gashler, M., Giraud-Carrier, C., Martinez, T.: Decision Tree Ensemble: Small Heterogeneous Is Better Than Large Homogeneous. In: 2008 Seventh International Conference on Machine Learning and Applications, ICMLA 2008, pp. 900–905 (2008)
Graczyk, M., Lasota, T., Trawiński, B.: Comparative Analysis of Premises Valuation Models Using KEEL, RapidMiner, and WEKA. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS (LNAI), vol. 5796, pp. 800–812. Springer, Heidelberg (2009)
Ho, T.K.: Random Decision Forest. In: 3rd International Conference on Document Analysis and Recognition, pp. 278–282 (1995)
Ho, T.K.: The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)
Kempa, O., Lasota, T., Telec, Z., Trawiński, B.: Investigation of Bagging Ensembles of Genetic Neural Networks and Fuzzy Systems for Real Estate Appraisal. In: Nguyen, N.T., Kim, C.-G., Janiak, A. (eds.) ACIIDS 2011, Part II. LNCS (LNAI), vol. 6592, pp. 323–332. Springer, Heidelberg (2011)
Kotsiantis, S.: Combining bagging, boosting, rotation forest and random subspace methods. Artificial Intelligence Review 35(3), 223–240 (2010)
Król, D., Lasota, T., Trawiński, B., Trawiński, K.: Investigation of Evolutionary Optimization Methods of TSK Fuzzy Model for Real Estate Appraisal. International Journal of Hybrid Intelligent Systems 5(3), 111–128 (2008)
Krzystanek, M., Lasota, T., Telec, Z., Trawiński, B.: Analysis of Bagging Ensembles of Fuzzy Models for Premises Valuation. In: Nguyen, N.T., Le, M.T., Świątek, J. (eds.) ACIIDS 2010. LNCS (LNAI), vol. 5991, pp. 330–339. Springer, Heidelberg (2010)
Kotsiantis, S.B., Pintelas, P.E.: Local Rotation Forest of Decision Stumps for Regression Problems. In: 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2009, pp. 170–174 (2009)
Lasota, T., Łuczak, T., Trawiński, B.: Investigation of Random Subspace and Random Forest Methods Applied to Property Valuation Data. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds.) ICCCI 2011, Part I. LNCS (LNAI), vol. 6922, pp. 142–151. Springer, Heidelberg (2011)
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 7(1), 3–16 (2010)
Lasota, T., Telec, Z., Trawiński, B., Trawiński, K.: Exploration of Bagging Ensembles Comprising Genetic Fuzzy Models to Assist with Real Estate Appraisals. In: Corchado, E., Yin, H. (eds.) IDEAL 2009. LNCS, vol. 5788, pp. 554–561. Springer, Heidelberg (2009)
Lasota, T., Telec, Z., Trawiński, B., Trawiński, K.: Investigation of the eTS Evolving Fuzzy Systems Applied to Real Estate Appraisal. Journal of Multiple-Valued Logic and Soft Computing 17(2-3), 229–253 (2011)
Lughofer, E., Trawiński, B., Trawiński, K., Kempa, O., Lasota, T.: On Employing Fuzzy Modeling Algorithms for the Valuation of Residential Premises. Information Sciences 181, 5123–5142 (2011)
Polikar, R.: Ensemble Learning. Scholarpedia 4(1), 2776 (2009)
Rodríguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation Forest: A New Classifier Ensemble Method. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(10), 1619–1630 (2006)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Zhang, C.-X., Zhang, J.-S., Wang, G.-W.: An empirical study of using Rotation Forest to improve regressors. Appl. Math. Comput. 195(2), 618–629 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lasota, T., Łuczak, T., Trawiński, B. (2012). Investigation of Rotation Forest Method Applied to Property Price Prediction. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29347-4_47
Download citation
DOI: https://doi.org/10.1007/978-3-642-29347-4_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29346-7
Online ISBN: 978-3-642-29347-4
eBook Packages: Computer ScienceComputer Science (R0)