Abstract
Following Breiman’s methodology, we propose the fundamentals to design and construct a “forest” of randomly generated fuzzy decision trees, i.e., a Fuzzy Random Forest. This approach combines the robustness of multi-classifiers, the construction efficiency of decision trees, the power of the randomness to increase the diversity of the trees in the forest, and the flexibility of fuzzy logic and the fuzzy sets for data managing. A prototype for the method has been constructed and we have implemented some specific strategies for inference in the Fuzzy Random Forest. Some experimental results are given.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ahn, H., Moon, H., Fazzari, J., Lim, N., Chen, J., Kodell, R.: Classification by ensembles from random partitions of high dimensional data. Computational Statistics and Data Analysis 51, 6166–6179 (2007)
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Cadenas, J.M., Garrido, M.C., Díaz-Valladares, R.A.: Hacia el Diseño y Construcción de un Fuzzy Random Forest. In: Proceedings of the II Simposio sobre Lógica Fuzzy y Soft Computing, pp. 41–48 (2007)
Cadenas, J.M., Garrido, M.C., Martinez España, R.: Generando etiquetas linguísticas: Un árbol de particiones. TR - DIIC 2/08, 1–20 (2008)
Debuse, J.C.W., Rayward-Smith, V.J.: Discretisation of Continuous Commercial Database Features for a Simulated Annealing Data Mining Algorithm. Applied Intelligence 11, 285–295 (1999)
Dietterich, T.G.: An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Machine Learning 40(2), 139–157 (2000)
Dougherty, J., Kohavi, R., Sahami, M.: Supervised and unsupervised discretization of continuous features. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 194–202 (1995)
Hamza, M., Larocque, D.: An empirical comparison of ensemble methods based on classification trees. Statistical Computati. & Simulation 75(8), 629–643 (2005)
Ho, T.K.: The random subspace method for constructing decision forests. Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)
Hothorn, T., Lausen, B.: Double-bagging: combining classifiers by bootstrap aggregation. Pattern Recognition 36(6), 1303–1309 (2003)
Jang, J.: Structure determination in fuzzy modeling: A Fuzzy CART approach. In: Proceedings of the IEEE Conference on Fuzzy Systems, pp. 480–485 (1994)
Janikow, C.Z.: Fuzzy decision trees: issues and methods. Transaction on Systems, Man and Cybernetics, Part B 28(1), 1–15 (1998)
Koen-Myung, L., Kyung-Mi, L., Jee-Hyong, L., Hyung, L.: A Fuzzy Decision Tree Induction Method for Fuzzy Data. In: Proceedings of the IEEE Conference on Fuzzy Systems, pp. 22–25 (1999)
Kuncheva, L.I.: A theorical study on six classifier fusion strategies. IEEE Transaction on PAMI 24(2), 281–286 (2002)
Kuncheva, L.I.: Fuzzy vs Non-fuzzy in combining classifiers designed by boosting. IEEE Transactions on Fuzzy Systems 11(6), 729–741 (2003)
Kuncheva, L.I.: Combining Pattern Classifiers - Methods and Algorithms. John Wiley and Sons, New Jersey (2004)
Martínez-Muñoz, G., Suárez, A.: Switching class labels to generate classification ensembles. Pattern Recognition 38(10), 1483–1494 (2005)
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)
Schapire, R.E.: The strength of weak learnability. Machine Learning 5(2), 197–227 (1990)
Segrera, S., Moreno, M.: An Experimental Comparative Study of Web Mining Methods for Recommender Systems. In: Proceedings of the 6th WSEAS Intern. Conference on Distance Learning and Web Engineering, pp. 56–61 (2006)
Witten, I.H., Frank, E.: Data Mining - Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Wolpert, D.: Stacked Generalization. Neural Networks 5, 241–259 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Bonissone, P.P., Cadenas, J.M., del Carmen Garrido, M., Díaz-Valladares, R.A. (2010). Fundamentals for Design and Construction of a Fuzzy Random Forest. In: Bouchon-Meunier, B., Magdalena, L., Ojeda-Aciego, M., Verdegay, JL., Yager, R.R. (eds) Foundations of Reasoning under Uncertainty. Studies in Fuzziness and Soft Computing, vol 249. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10728-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-10728-3_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10726-9
Online ISBN: 978-3-642-10728-3
eBook Packages: EngineeringEngineering (R0)