Laser & Optoelectronics Progress, Volume. 59, Issue 23, 2306003(2022)
Fiber Intrusion Signal Classification Based on Gradient Boosting Decision Tree Algorithm
Fig. 1. Original signal of tapping and the decomposition result. (a) Original signal; (b) decomposition result
Fig. 2. Original fiber intrusion signal and preprocessing results. (a) Original signal; (b) preprocessing result
Fig. 3. Flowchart of the AdaBoost algorithm
Fig. 4. Validation curves of GBDT algorithm under different parameters. (a) n_estimators; (b) learning_rate; (c) max_depth; (d) subsample
Fig. 5. AdaBoost algorithm grid search validation curve. (a) n_estimators; (b) learning_rate
Fig. 6. Grid search verification curve of the SVM algorithm. (a) C; (b) gamma
Fig. 7. Confusion matrices for different algorithms. (a) GBDT algorithm; (b) DT-AdaBoost algorithm; (c) SVM algorithm
Fig. 8. Recognition rate of fiber intrusion signals for different algorithms
|
|
Get Citation
Copy Citation Text
Hongquan Qu, Zhengyi Wang, Zhiyong Sheng, Hongbin Qu, Ling Wang. Fiber Intrusion Signal Classification Based on Gradient Boosting Decision Tree Algorithm[J]. Laser & Optoelectronics Progress, 2022, 59(23): 2306003
Category: Fiber Optics and Optical Communications
Received: Sep. 9, 2021
Accepted: Nov. 19, 2021
Published Online: Nov. 29, 2021
The Author Email: Wang Zhengyi (415896430@qq.com)