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