Transformed Tree-Structured Regression Method

Gheno, Gloria

doi:10.18052/www.scipress.com/BMSA.16.70

BMSA > BMSA Volume 16 > Transformed Tree-Structured Regression Method

< Back to Volume

Transformed Tree-Structured Regression Method

Full Text PDF

Abstract:

Many times the response variable is linked linearly to the function of the regressors and to the error term through its function g(Y). For this reason the traditional tree-structured regression methods do not understand the real relationship between the regressors and the dependent variable. I derive a modified version of the most popular tree-structured regression methods to consider this situation of nonlinearity. My simulation results show that my method with regression tree is better than the tree-based regression methods proposed in literature because it understands the true relationship between the regressors and the dependent variable also when it is not possible to divide exactly the error part from the regressors part.

Info:

Periodical:

Bulletin of Mathematical Sciences and Applications (Volume 16)

Pages:

70-75

DOI:

https://doi.org/10.18052/www.scipress.com/BMSA.16.70

Citation:

G. Gheno, "Transformed Tree-Structured Regression Method", Bulletin of Mathematical Sciences and Applications, Vol. 16, pp. 70-75, 2016

Online since:

August 2016

Authors:

Gloria Gheno

Keywords:

Birckel-Docksum Transformation, Nonlinearity, Random Forest, Regression Tree

Export:

RIS, BibTeX, Text

Distribution:

Open Access

This work is licensed under a
Creative Commons Attribution 4.0 International License

References:

[1] P.J. Bickel, K.A. Doksum, An analysis of transformations revisited. Journal of the American Statistical Association, 76 (1981) 296-231.

[2] L. Breiman, Bagging predictors. Machine learning, 24 (1996) 123-140.

[3] L. Breiman, Random Forest. Machine Learning , 45 (2001) 5-32.

[4] L. Breiman, J.H. Freidman, R.A. Olshen, and C.J. Stone, Classification and regression trees. Wadsworth, Belmont CA, (1984).

[5] J. McCall, Genetic algorithms for modelling and optimisation. Journal of Computational and Applied Mathematics, 184 (2005) 205-222.

[6] J.H. Holland JH, Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, (1975).

[7] S. N. Sivanandam, S.N. Deepa, Introduction to Genetic Algorithms. Springer-Verlag, Berlin, (2007).

Show More Hide

Cited By:

This article has no citations.