Summary
This paper presents a novel instance-space decomposition framework for decision trees. According to this framework, the original instance-space is decomposed into several subspaces in a parallel-to-axis manner. A different classifier is assigned to each subspace. Subsequently, an unlabelled instance is classified by employing the appropriate classifier based on the subspace where the instance belongs. An experimental study which was conducted in order to compare various implementations of this framework indicates that previously presented implementations can be improved both in terms of accuracy and computation time.
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
Dietterich, T.G. (1998). Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10(7).
Esmeir S. and Markovitch S. (2004). Lookahead-based Algorithms for Anytime Induction of Decision Trees. In Proceedings of The Twenty-First International Conference on Machine Learning, Banff, Alberta, Canada. Morgan Kaufmann, pp. 257–264.
Kohavi R. (1996). Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 202–207.
Maimon O. and Rokach L. (2005) Decomposition Methodology for Knowledge Discovery and Data Mining: Theory and Applications Series in Machine Perception and Artificial Intelligence — Vol. 61 World Scientific Publishing.
Mertz, C. J. (1999). Using Correspondence Analysis to Combine Classifiers. Machine Learning 36(1), 33–58.
Murthy, S. and Salzberg, S. (1995), Lookahead and pathology in decision tree induction, in C. S. Mellish, ed., Proceedings of the 14th International Joint Conference on Artificial Intelligence, Morgan Kaufmann, pp. 1025–1031
Quinlan, J.R. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann.
Rokach L., Maimon O. (2005): Top-down induction of decision trees classifiers-a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C 35(4):476–487.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Cohen, S., Rokach, L., Maimon, O. (2006). A Decision-Tree Framework for Instance-space Decomposition. In: Last, M., Szczepaniak, P.S., Volkovich, Z., Kandel, A. (eds) Advances in Web Intelligence and Data Mining. Studies in Computational Intelligence, vol 23. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-33880-2_27
Download citation
DOI: https://doi.org/10.1007/3-540-33880-2_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33879-6
Online ISBN: 978-3-540-33880-2
eBook Packages: EngineeringEngineering (R0)