Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications

Random Forest Regression Based on Partial Least Squares Connect Partial Least Squares and Random Forest

Authors
Zhulin Hao, Jianqiang Du, Bin Nie, Fang Yu, Riyue Yu, Wangping Xiong
Corresponding Author
Zhulin Hao
Available Online January 2016.
DOI
https://doi.org/10.2991/icaita-16.2016.48How to use a DOI?
Keywords
partial least square; regression tree; random forest; linear approximation; TCM information
Abstract
Partial Least Squares (PLS) Regression is lack of theoretical guidance of rules to achieve the nonlinear by the quasi linearization rule, and its accuracy declines in the face of the unknown variables distribution. Furthermore, the loss of information is easy to arise for the mean processing of the leaf in the Regression Tree of the traditional Random Forest Regression. On this basis, Partial Model Tree (PMT) is proposed combining Partial Least Squares Regression with Regression Tree, to achieve the nonlinear regression by constructing multiple linear fragments of Partial Least Squares to complete linear approximation of the unknown variables, and the information loss issue caused by that the leaf nodes are treated by direct mean processing is avoided, when PLS regression is used in the leaf nodes. It applies PMT to ensemble learning to build Partial Least Squares of Random Forests Regression (PLS-RFR), improving the generalization ability of PMT. The ability of explanation and predicting get improved in the experiment data of MaXingShiGan decoction of the monarch drug to treat the asthma or cough and five sample sets in the UCI Machine Learning Repository. Finally, it verifies that the PMT and RF-PLS possess a certain degree of validity and correctness.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
Series
Advances in Intelligent Systems Research
Publication Date
January 2016
ISBN
978-94-6252-162-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/icaita-16.2016.48How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Zhulin Hao
AU  - Jianqiang Du
AU  - Bin Nie
AU  - Fang Yu
AU  - Riyue Yu
AU  - Wangping Xiong
PY  - 2016/01
DA  - 2016/01
TI  - Random Forest Regression Based on Partial Least Squares Connect Partial Least Squares and Random Forest
BT  - Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
PB  - Atlantis Press
SP  - 191
EP  - 196
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaita-16.2016.48
DO  - https://doi.org/10.2991/icaita-16.2016.48
ID  - Hao2016/01
ER  -