Abstract
The ensemble machine learning methods incorporating bagging, random subspace, random forest, and rotation forest employing decision trees, i.e. Pruned Model Trees, as base learning algorithms were developed in WEKA environment. The methods were applied to the real-world regression problem of predicting the prices of residential premises based on historical data of sales/purchase transactions. The accuracy of ensembles generated by the methods was compared for several levels of noise injected into an attribute, output, and both attribute and output. Ensembles built using rotation forest outperformed other models. In turn, random subspace method resulted in the models that were the most resistant to noised data.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Skurichina, M., Raudys, S., Duin, R.P.W.: K-Nearest Neighbors Directed Noise Injection in Multilayer Perceptron Training. IEEE Transactions on Neural Networks 11(2), 504–511 (2000)
Nettleton, D.F., Orriols-Puig, A., Fornells, A.: A study of the effect of different types of noise on the precision of supervised learning techniques. Artificial Intelligence Review 33(4), 275–306 (2010)
Zhu, X., Wu, X.: Class Noise vs. Attribute Noise: A Quantitative Study of Their Impacts. Artificial Intelligence Review 22, 177–210 (2004)
Sáez, J.A., Luengo, J., Herrera, F.: Fuzzy Rule Based Classification Systems versus Crisp Robust Learners Trained in Presence of Class Noise’s Effects: A Case of Study. In: 11th International Conference on Intelligent Systems Design and Applications (ISDA 2011), Córdoba, Spain, pp. 1229–1234 (2011)
Khoshgoftaar, T.M., Van Hulse, J., Napolitano, A.: Comparing Boosting and Bagging Techniques With Noisy and Imbalanced Data With Noisy and Imbalanced Data. IEEE Transactions on System, Man, and Cybernetics–Part A: Systems and Humans 41(3), 552–568 (2011)
Lasota, T., Telec, Z., Trawiński, B., Trawiński, G.: Investigation of Random Subspace and Random Forest Regression Models Using Data with Injected Noise. In: Graña, M., Toro, C., Howlett, R.J., Jain, L.C. (eds.) KES 2012. LNCS (LNAI), vol. 7828, pp. 1–10. Springer, Heidelberg (2013)
Graczyk, M., Lasota, T., Trawiński, B., Trawiński, K.: Comparison of Bagging, Boosting and Stacking Ensembles Applied to Real Estate Appraisal. In: Nguyen, N.T., Le, M.T., Świątek, J. (eds.) ACIIDS 2010, Part II. LNCS, vol. 5991, pp. 340–350. Springer, Heidelberg (2010)
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)
Lasota, T., Telec, Z., Trawiński, G., Trawiński, B.: Empirical Comparison of Resampling Methods Using Genetic Fuzzy Systems for a Regression Problem. In: Yin, H., Wang, W., Rayward-Smith, V. (eds.) IDEAL 2011. LNCS, vol. 6936, pp. 17–24. Springer, Heidelberg (2011)
Lasota, T., Telec, Z., Trawiński, G., Trawiński, B.: Empirical Comparison of Resampling Methods Using Genetic Neural Networks for a Regression Problem. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds.) HAIS 2011, Part II. LNCS (LNAI), vol. 6679, pp. 213–220. Springer, Heidelberg (2011)
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, vol. 6922, pp. 142–151. Springer, Heidelberg (2011)
Lasota, T., Telec, Z., Trawiński, B., Trawiński, G.: Investigation of Rotation Forest Ensemble Method Using Genetic Fuzzy Systems for a Regression Problem. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ACIIDS 2012, Part I. LNCS, vol. 7196, pp. 393–402. Springer, Heidelberg (2012)
Lasota, T., Łuczak, T., Trawiński, B.: 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.) ICAISC 2012, Part I. LNCS, vol. 7267, pp. 403–411. Springer, Heidelberg (2012)
Breiman, L.: Bagging Predictors. Machine Learning 24(2), 123–140 (1996)
Ho, T.K.: The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)
Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)
Rodrígeuz, J.J., Kuncheva, 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., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, San Francisco (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lasota, T., Łuczak, T., Niemczyk, M., Olszewski, M., Trawiński, B. (2013). Investigation of Property Valuation Models Based on Decision Tree Ensembles Built over Noised Data. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2013. Lecture Notes in Computer Science(), vol 8083. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40495-5_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-40495-5_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40494-8
Online ISBN: 978-3-642-40495-5
eBook Packages: Computer ScienceComputer Science (R0)