A new random forest method based on belief decision trees and its application in intention estimation

Xinyu Li, Mingda Li, Yu Zhang, Xinyang Deng

科研成果: 书/报告/会议事项章节会议稿件同行评审

4 引用 (Scopus)

摘要

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.

源语言英语
主期刊名Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021
出版商Institute of Electrical and Electronics Engineers Inc.
6008-6012
页数5
ISBN(电子版)9781665440899
DOI
出版状态已出版 - 2021
活动33rd Chinese Control and Decision Conference, CCDC 2021 - Kunming, 中国
期限: 22 5月 202124 5月 2021

出版系列

姓名Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021

会议

会议33rd Chinese Control and Decision Conference, CCDC 2021
国家/地区中国
Kunming
时期22/05/2124/05/21

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