Application of Decision Tree Integrated Hybrid Classifier in Feature-Fused Robot Big Data
Authors: 
Ziheng Li, Jiankai Zuo, Jiehong Wu, Zeyuan Liu, Yifan Song, Shenglin GengAuthors Info & Claims
Ziheng Li, Jiankai Zuo, Jiehong Wu, Zeyuan Liu, Yifan Song, Shenglin GengAuthors Info & ClaimsPages 681 - 686
Abstract
LightGBM is a fast distributed, high-performance gradient boosting framework based on decision tree algorithm, which can be used for sorting, classification, regression and many other machine learning tasks. In order to realize the classification of the terrain environment where the robot is located, this paper uses the gradient boosting tree LightGBM to identify and classify the IMU signals of the robot feet. First of all, the quaternion Euler transform and segmentation are performed on the signal. Secondly, the different period segments of the signal are analyzed and feature fused from the time domain and frequency domain respectively. Finally, the decision tree integrated hybrid classifier with the best parameters, namely LightGBM, is employed to classify the robot on the ground. The experimental results show that the accuracy of this method for the recognition of robot IMU signals on different surfaces reaches 90.7%.
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Index Terms
- Application of Decision Tree Integrated Hybrid Classifier in Feature-Fused Robot Big Data
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Published In
June 2021
807 pages
ISBN:9781450390231
DOI:10.1145/3473714
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- Chongqing Univ.: Chongqing University
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Association for Computing Machinery
New York, NY, United States
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Published: 13 August 2021
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ICCIR 2021
ICCIR 2021: 2021 International Conference on Control and Intelligent Robotics
June 18 - 20, 2021
Guangzhou, China
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ICCIR '21 Paper Acceptance Rate 131 of 239 submissions, 55%;
Overall Acceptance Rate 131 of 239 submissions, 55%
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