TY - GEN
T1 - A new random forest method based on belief decision trees and its application in intention estimation
AU - Li, Xinyu
AU - Li, Mingda
AU - Zhang, Yu
AU - Deng, Xinyang
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Random forest algorithm is a classification and prediction model, which is used in many fields. Random forest is composed of multiple decision trees. In the face of more and more complex uncertain environments, ordinary decision trees can no longer meet the requirements, so belief trees based on belief functions appear. This paper proposes a new random forest method based on belief trees. Compared with ordinary random forest in which voting or average method is used to combine the result of each decision tree, the proposed method fully considers the influence of the weight of each tree, and combine the result of each belief tree through a weighted averaging combination of belief structures. In order to demonstrate the effectiveness of the proposed method, it is used in intention estimation. The results show that the accuracy of intention recognition is improved by using the proposed method compared with original random forest algorithm.
AB - Random forest algorithm is a classification and prediction model, which is used in many fields. Random forest is composed of multiple decision trees. In the face of more and more complex uncertain environments, ordinary decision trees can no longer meet the requirements, so belief trees based on belief functions appear. This paper proposes a new random forest method based on belief trees. Compared with ordinary random forest in which voting or average method is used to combine the result of each decision tree, the proposed method fully considers the influence of the weight of each tree, and combine the result of each belief tree through a weighted averaging combination of belief structures. In order to demonstrate the effectiveness of the proposed method, it is used in intention estimation. The results show that the accuracy of intention recognition is improved by using the proposed method compared with original random forest algorithm.
KW - Belief decision tree
KW - Evidence combination
KW - Intention estimation
KW - Random forest
UR - http://www.scopus.com/inward/record.url?scp=85125175192&partnerID=8YFLogxK
U2 - 10.1109/CCDC52312.2021.9602761
DO - 10.1109/CCDC52312.2021.9602761
M3 - 会议稿件
AN - SCOPUS:85125175192
T3 - Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021
SP - 6008
EP - 6012
BT - Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 33rd Chinese Control and Decision Conference, CCDC 2021
Y2 - 22 May 2021 through 24 May 2021
ER -