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%.
References
[1]
TIANQI C, CARLOS G. XGBoost: A Scalable Tree Boosting System. The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Pages 785--794, 2016.
[2]
J. Xie, Z. Li, Z. Zhou and S. Liu, "A Novel Bearing Fault Classification Method Based on XGBoost: The Fusion of Deep Learning-Based Features and Empirical Features, " in IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1--9, 2021.
[3]
J. Zhong, Y. Sun, W. Peng, M. Xie, J. Yang and X. Tang, "XGBFEMF: An XGBoost-Based Framework for Essential Protein Prediction, " in IEEE Transactions on NanoBioscience, vol.17, no.3, pp. 243--250, 2018.
[4]
X. Gu, Y. Han and J. Yu, "A Novel Lane-Changing Decision Model for Autonomous Vehicles Based on Deep Autoencoder Network and XGBoost, " in IEEE Access, vol. 8, pp. 9846--9863, 2020.
[5]
P. Liu, B. Fu, S. X. Yang, L. Deng, X. Zhong and H. Zheng, "Optimizing Survival Analysis of XGBoost for Ties to Predict Disease Progression of Breast Cancer, " in IEEE Transactions on Biomedical Engineering, vol. 68, no. 1, pp. 148--160, Jan. 2021.
[6]
X. Gu, Y. Han and J. Yu, "A Novel Lane-Changing Decision Model for Autonomous Vehicles Based on Deep Autoencoder Network and XGBoost, " in IEEE Access, vol. 8, pp. 9846--9863, 2020.
[7]
Y. Song, J. Zuo, J. Wu, Z. Liu and Z. Li, "Robot Perceptual Classification Method Based on Mixed Features of Decision Tree and Random Forest, " 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), 2021, pp. 919--922.
[8]
A. Ogunleye and Q. -G. Wang, "XGBoost Model for Chronic Kidney Disease Diagnosis, " in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.17, no.6, pp.2131-2140, 2020.
[9]
D. Zhang and Y. Gong, "The Comparison of LightGBM and XGBoost Coupling Factor Analysis and Prediagnosis of Acute Liver Failure, " in IEEE Access, vol. 8, pp. 220990--221003, 2020.
[10]
Y. Ju, G. Sun, Q. Chen, M. Zhang, H. Zhu and M. U. Rehman, "A Model Combining Convolutional Neural Network and LightGBM Algorithm for Ultra-Short-Term Wind Power Forecasting, " in IEEE Access, vol. 7, pp. 28309--28318, 2019.
[11]
R. Punmiya and S. Choe, "Energy Theft Detection Using Gradient Boosting Theft Detector With Feature Engineering-Based Preprocessing, " in IEEE Transactions on Smart Grid, vol. 10, no. 2, pp. 2326--2329, March 2019.
[12]
T. Deng, Y. Zhao, S. Wang and H. Yu, "Sales Forecasting Based on LightGBM, " 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), Guangzhou, China, 2021, pp. 383--386.
[13]
Y. WANG et al., "Short-Term Load Forecasting for Industrial Customers Based on TCN-LightGBM, " in IEEE Transactions on Power Systems.
[14]
N. Qu, J. Zuo, J. Chen and Z. Li, "Series Arc Fault Detection of Indoor Power Distribution System Based on LVQ-NN and PSO-SVM, " in IEEE Access, vol. 7, pp. 184020--184028, 2019.
[15]
Y. Shin, S. Kim, J. Chung, H. S. Chung, S. G. Han and J. Cho, "Emergency Department Return Prediction System using Blood Samples with LightGBM for Smart Health Care Services, " in IEEE Consumer Electronics Magazine.
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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