Two-Stage Constructing Hyper-Plane for Each Test Node of Decision Tree

Wei She; Hong Li; Guo Qing Yu; Rui Deng

doi:10.4028/www.scientific.net/AMM.26-28.776

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 26-28Two-Stage Constructing Hyper-Plane for Each Test...

Two-Stage Constructing Hyper-Plane for Each Test Node of Decision Tree

Abstract:

How to construct the “appropriate” split hyper-plane in test nodes is the key of building decision trees. Unlike a univariate decision tree, a multivariate (oblique) decision tree could find the hyper-plane that is not orthogonal to the features’ axes. 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 tree (TSDT) induced in this way keeps the interpretability of univariate decision trees and the trait of multivariate decision trees which could find oblique hyper-plane. The tests of the impact of Combination methods tell us that TSDT based combination algorithm is much better than other tree based combination methods in accuracy.

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Periodical:

Applied Mechanics and Materials (Volumes 26-28)

Pages:

776-779

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.26-28.776

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Online since:

June 2010

Authors:

Wei She, Hong Li, Guo Qing Yu, Rui Deng

Keywords:

Hyper-Plane, TSDT, Two Stages Decision Tree

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References

[1] Su, X.G., Tsai, C. -L., & Wang, C. Tree-structured model diagnostics for linear regression. Mach Learn 74(2009): 111-131.

DOI: 10.1007/s10994-008-5080-8

Google Scholar

[2] Vens, C., Struyf, J., Schietgat, L., Dzeroski, S., & Blockeel, H. Decision trees for hierarchical multi-label classification. Mach Learn, 73 (2008): 185-214.

DOI: 10.1007/s10994-008-5077-3

Google Scholar

[3] Quinlan, J.R. Discovering rules by induction from large collection of examples. In D. Michie, editor, Expert systems in the Micro Electronic Age. Edinburgh University Press, Edinburgh, UK (1979).

Google Scholar

[4] Quinlan J R. C4. 5: Programs for Machine Learning [M]. San Mateo, CA: Morgan Kaufmann (1993).

Google Scholar

[5] Breiman, L., Friedman, J.H., Olshen, R., & Stone, C.J. Classification and Regression Trees. Chapman & Hall, New York (1984).

Google Scholar

[6] Brodley, C.E., & Utgoff, P.E. Multivariate decision trees. Machine Learning, 19(1995), 45-77.

DOI: 10.1007/bf00994660

Google Scholar

[7] 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).

Google Scholar

[8] Gama, J. Functional Trees. Machine Learning, 55 (2004), 219-250.

Google Scholar