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

DOI

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.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - 10.2991/icaita-16.2016.48
ID  - Hao2016/01
ER  -