Abstract
How to construct the “appropriate” split hyper-plane in test nodes is the key of building decision trees. In this paper, we re-explain the process of building test nodes in terms of geometry. Based on this, we propose a method of learning the hyper-plane with two stages. The first stage searches for appropriate normal direction based on unsupervised methods (e.g. PCA, ICA etc), or supervised methods (e.g. neural network). The second stage detects the intercept of the hyper-plane in the normal direction according to some criterions, such as Gini of CART and information gain ratio (GainRatio) of C4.5. The experimental results conform that TSDT can improve the accuracy of univariate trees and that it is quite comparable to functional tree proposed by Gama, J. 2004, yet, needing much less learning time.
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References
wSu, X.G., Tsai, C.-L., Wang, C.: Tree-structured model diagnostics for linear regression. Mach. Learn. 74, 111–131 (2009)
Vens, C., Struyf, J., Schietgat, L., Dzeroski, S., Blockeel, H.: Decision trees for hierarchical multi-label classification. Mach. Learn. 73, 185–214 (2008)
Quinlan, J.R.: Discovering rules by induction from large collection of examples. In: Michie, D. (ed.) Expert systems in the Micro Electronic Age, Edinburgh University Press, Edinburgh (1979)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Breiman, L., Friedman, J.H., Olshen, R., Stone, C.J.: Classification and Regression Trees. Chapman & Hall, New York (1984)
Brodley, C.E., Utgoff, P.E.: Multivariate decision trees. Machine Learning 19, 45–77 (1995)
Nilsson, N.J.: Learning machines. McGraw-Hill, New York (1965)
Duda, R.O., Hart, P.E.: Pattern classification and scene analysis. Wiley & Sons, New York (1973)
Utgoff, P.E., Brodley, C.E.: An incremental method for finding multivariate splits for decision trees. In: Proceedings of the Seventh International Conference on Machine Learning, pp. 58–65. Morgan Kaufmann, Austin (1990)
Utgoff, P.E., Brodley, C.E.: Linear machine decision trees (COINS Technical Report 91-10), Amherst, MA: University of Massachusetts, Department of Computer and Information Science (1991)
Gama, J.: Probabilistic linear tree. In: Fisher, D. (ed.) Proc. of the 14th International Conference on Machine Learning, pp. 134–142. Morgan Kaufmann, San Francisco (1997)
Gama, J.: Functional Trees. Machine Learning 55, 219–250 (2004)
Murthy, S., Kasif, S., Salzberg, S.: A system for induction of oblique decision trees. Journal of Artificial Intelligence Research 2, 1–32 (1994)
Liu, H., Setiono, R.: Feature Transformation and Multivariate Decision Tree Induction. Discovery Science. Springer, Heidelberg (1998)
Zhong, M.Y., Georgiopoulos, M., Anagnostopoulos, G.: A k-norm pruning algorithm for decision tree classifiers based on error rate estimation. Mach. Learning 71, 55–88 (2008)
Blockeel, H., De Raedt, L., Ramon, J.: Top-down induction of clustering trees. In: Proceedings of the 15th International Conference on Machine Learning, pp. 55–63 (1998)
Rodriguez, J., Kuncheva, L.: Rotation Forest: A New Classifier Ensemble Method. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 1613–1619 (2006)
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Zhang, S., She, W., Wang, M., Duan, Z. (2011). A Two-Stage Decision Tree Algorithm on Constructing Hyper-Plane. In: Zeng, D. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23220-6_40
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DOI: https://doi.org/10.1007/978-3-642-23220-6_40
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