Abstract
This paper proposes a pre-pruning method called KLC4 for decision trees, and our method, based on KL divergence, drops candidate attributes irrelevant to classification. We compare our technique to conventional ones, and show usefulness of our technique by experiments.
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
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: VLDB 1994 (1994)
Fürnkranz, J.: Pre-Pruning and Post-Pruning. Machine Learning 27-2, 139–171 (1997)
Furukawa, K., Ozaki, T., et al.: Inductive Logic Programming, Kyoritsu (2001)
Han, J., Kamber, M.: Data Mining - Concepts and Techniques. Morgan Kaufmann, San Francisco (2000)
Holte, R.C.: Very Simple Classification Rules Perform Well on Most Commonly Used Datasets. Machine Learning 11-1 (1993)
Liu, B., Hsu, W., Ma, Y.: Integrating Classification and Association Rule Mining. In: Proc. Conference on Knowledge Discovery and Data Mining, pp. 80–86 (1998)
Pfahringer, B.: Inducing Small and Accurate Decision Trees, Technical Report TR-98-09, Oesterreichisches Forschungsinstitut für Artificial Intelligence, Wien (1998)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics 21(3), 660–674 (1991)
Takamitsu, T., Miura, T., Shioya, I.: Testing Structure of Decision Trees. In: Proc. Info. Syst. and Engr.(ISE) (2002)
Takamitsu, T., Miura, T., Shioya, I.: Decision Trees using Class Hierarchy. In: Proc. Hybrid Intelligent System, HIS (2003)
Terabe, M., Katai, O., Sawaragi, T., Washio, T., Motoda, H.: Attribute Generation Based on Association Rules. J. of JSAI 15-1 (2000)
UCI Machine Learning Repository (2004), http://www1.ics.uci.edu/~mlearn/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Takamitsu, T., Miura, T., Shioya, I. (2004). Pre-pruning Decision Trees by Local Association Rules. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_21
Download citation
DOI: https://doi.org/10.1007/978-3-540-28651-6_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22881-3
Online ISBN: 978-3-540-28651-6
eBook Packages: Springer Book Archive