Abstract
Decision trees are among the most effective and interpretable classification algorithms while ensembles techniques have been proven to alleviate problems regarding over-fitting and variance. On the other hand, decision trees show a tendency to lack stability given small changes in the data, whereas interpreting an ensemble of trees is challenging to comprehend. We propose the technique of Ensemble-Trees which uses ensembles of rules within the test nodes to reduce over-fitting and variance effects. Validating the technique experimentally, we find that improvements in performance compared to ensembles of pruned trees exist, but also that the technique does less to reduce structural instability than could be expected.
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
Breiman, L., Friedman, J., Stone, C.J., Olshen, R.: Classification and Regression Tree. Chapman & Hall, New York (1984)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Blockeel, H., De Raedt, L.: Top-down induction of first-order logical decision trees. Artif. Intell. 101, 285–297 (1998)
Murthy, S.K.: On Growing Better Decision Trees from Data. PhD thesis, John Hopkins University, Baltimore, Maryland, USA (1997)
Kohavi, R., Kunz, C.: Option decision trees with majority votes. In: Fisher, D.H. (ed.) ICML, pp. 161–169. Morgan Kaufmann, San Francisco (1997)
Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)
Zaki, M.J., Aggarwal, C.C.: XRules: an effective structural classifier for XML data. In: Getoor, L., Senator, T.E., Domingos, P., Faloutsos, C. (eds.) KDD, Washington, DC, USA, pp. 316–325. ACM, New York (2003)
Lindgren, T., Boström, H.: Resolving rule conflicts with double induction. In: Berthold, M.R., Lenz, H.-J., Bradley, E., Kruse, R., Borgelt, C. (eds.) IDA 2003. LNCS, vol. 2810, pp. 60–67. Springer, Heidelberg (2003)
Morishita, S., Sese, J.: Traversing itemset lattices with statistical metric pruning. In: PODS, Dallas, Texas, USA, pp. 226–236. ACM, New York (2000)
Geamsakul, W., Matsuda, T., Yoshida, T., Motoda, H., Washio, T.: Performance evaluation of decision tree graph-based induction. In: Grieser, G., Tanaka, Y., Yamamoto, A. (eds.) DS 2003. LNCS (LNAI), vol. 2843, pp. 128–140. Springer, Heidelberg (2003)
Bringmann, B., Zimmermann, A.: Tree2 - decision trees for tree structured data. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 46–58. Springer, Heidelberg (2005)
Breiman, L.: Bagging predictors. Machine Learning 24, 123–140 (1996)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119–139 (1997)
Galiano, F.B., Cubero, J.C., Sánchez, D., Serrano, J.M.: Art: A hybrid classification model. Machine Learning 54, 67–92 (2004)
Frank, E., Witten, I.H.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Berlin Heidelberg
About this paper
Cite this paper
Zimmermann, A. (2008). Ensemble-Trees: Leveraging Ensemble Power Inside Decision Trees. In: Jean-Fran, JF., Berthold, M.R., Horváth, T. (eds) Discovery Science. DS 2008. Lecture Notes in Computer Science(), vol 5255. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88411-8_10
Download citation
DOI: https://doi.org/10.1007/978-3-540-88411-8_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88410-1
Online ISBN: 978-3-540-88411-8
eBook Packages: Computer ScienceComputer Science (R0)