Hyperparameter Optimization Method Based on Harmony Search Algorithm to Improve Performance of 1D CNN Human Respiration Pattern Recognition System

In this study, we propose a method to find an optimal combination of hyperparameters to improve the accuracy of respiration pattern recognition in a 1D (Dimensional) convolutional neural network (CNN). The proposed method is designed to integrate with a 1D CNN using the harmony search algorithm. In...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-07, Vol.20 (13), p.3697
Main Authors: Kim, Seong-Hoon, Geem, Zong Woo, Han, Gi-Tae
Format: Article
Language:English
Subjects:
1D convolutional neural network
Algorithms
harmony search algorithm
Humans
hyperparameter optimization
Neural Networks, Computer
Pattern Recognition, Automated
Respiration
respiration patterns
ultra-wideband radar
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Summary:In this study, we propose a method to find an optimal combination of hyperparameters to improve the accuracy of respiration pattern recognition in a 1D (Dimensional) convolutional neural network (CNN). The proposed method is designed to integrate with a 1D CNN using the harmony search algorithm. In an experiment, we used the depth of the convolutional layer of the 1D CNN, the number and size of kernels in each layer, and the number of neurons in the dense layer as hyperparameters for optimization. The experimental results demonstrate that the proposed method provided a recognition rate for five respiration patterns of approximately 96.7% on average, which is an approximately 2.8% improvement over an existing method. In addition, the number of iterations required to derive the optimal combination of hyperparameters was 2,000,000 in the previous study. In contrast, the proposed method required only 3652 iterations.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20133697