Features gradient-based signals selection algorithm of linear complexity for convolutional neural networks
-
Published:2024
Issue:1
Volume:9
Page:792-817
-
ISSN:2473-6988
-
Container-title:AIMS Mathematics
-
language:
-
Short-container-title:MATH
Author:
Omae Yuto1, Sakai Yusuke2, Takahashi Hirotaka2
Affiliation:
1. College of Industrial Technology, Nihon University, 1-2-1, Izumi, Narashino, Chiba 275-8575, Japan 2. Research Center for Space Science, Advanced Research Laboratories and Department of Design and Data Science, Tokyo City University, Kanagawa 224-8551, Japan
Abstract
<abstract><p>Recently, convolutional neural networks (CNNs) for classification by time domain data of multi-signals have been developed. Although some signals are important for correct classification, others are not. The calculation, memory, and data collection costs increase when data that include unimportant signals for classification are taken as the CNN input layer. Therefore, identifying and eliminating non-important signals from the input layer are important. In this study, we proposed a features gradient-based signals selection algorithm (FG-SSA), which can be used for finding and removing non-important signals for classification by utilizing features gradient obtained by the process of gradient-weighted class activation mapping (grad-CAM). When we defined $ n_ \mathrm{s} $ as the number of signals, the computational complexity of FG-SSA is the linear time $ \mathcal{O}(n_ \mathrm{s}) $ (i.e., it has a low calculation cost). We verified the effectiveness of the algorithm using the OPPORTUNITY dataset, which is an open dataset comprising of acceleration signals of human activities. In addition, we checked the average of 6.55 signals from a total of 15 signals (five triaxial sensors) that were removed by FG-SSA while maintaining high generalization scores of classification. Therefore, FG-SSA can find and remove signals that are not important for CNN-based classification. In the process of FG-SSA, the degree of influence of each signal on each class estimation is quantified. Therefore, it is possible to visually determine which signal is effective and which is not for class estimation. FG-SSA is a white-box signal selection algorithm because it can understand why the signal was selected. The existing method, Bayesian optimization, was also able to find superior signal sets, but the computational cost was approximately three times greater than that of FG-SSA. We consider FG-SSA to be a low-computational-cost algorithm.</p></abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Subject
General Mathematics
Reference57 articles.
1. N. Shahini, Z. Bahrami, S. Sheykhivand, S. Marandi, M. Danishvar, S. Danishvar, et al., Automatically identified EEG signals of movement intention based on CNN network (end-to-end), Electronics, 11 (2022), 3297. https://doi.org/10.3390/electronics11203297 2. T. Zebin, P. J. Scully, K. B. Ozanyan, Human activity recognition with inertial sensors using a deep learning approach, Proceedings IEEE Sensors, (2017), 1–3. https://doi.org/10.1109/ICSENS.2016.7808590 3. W. Xu, Y. Pang, Y. Yang, Y. Liu, Human activity recognition based on convolutional neural network, Proceedings of the International Conference on Pattern Recognition, (2018), 165–170. https://doi.org/10.1109/ICPR.2018.8545435 4. Y. Omae, M. Kobayashi, K. Sakai, T. Akiduki, A. Shionoya, H. Takahashi, Detection of swimming stroke start timing by deep learning from an inertial sensor, ICIC Express Letters Part B: Applications ICIC International, 11 (2020), 245–251. https://doi.org/10.24507/icicelb.11.03.245 5. D. Sagga, A. Echtioui, R. Khemakhem, M. Ghorbel, Epileptic seizure detection using EEG signals based on 1D-CNN approach, Proceedings of the 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, (2020), 51–56. https://doi.org/10.1109/STA50679.2020.9329321
|
|