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Journal Article
Label-preserving Data Augmentation for Mobile Sensor Data
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- Authors
- Mooseop Kim, Chi Yoon Jeong
- Issue Date
- 2021-01
- Citation
- Multidimensional Systems and Signal Processing, v.32, no.1, pp.115-129
- ISSN
- 0923-6082
- Publisher
- Springer
- Language
- English
- Type
- Journal Article
- Abstract
- Data augmentation is important for training neural networks, especially when there is not enough data to train a network well. However, data augmentation that results in the loss of label information may reduce the performance of the model. Most conventional data augmentation methods have been developed for image- or sound-related tasks, in which case the label information of the augmented data is easily and intuitively verified by human observation. However, in the case of sensor signals, it is difficult to recognize whether there is a change in the label information of the augmented data. We propose a systematic data augmentation method to maximize the performance by automatically finding the range of augmentation that preserves the labels of the augmented data. The experimental results show that the proposed method to extract the label-preserving range is practical and that the retrained model using data augmented within this range improves the performance by at least 5% without the need to further optimize the model architecture.
- KSP Keywords
- Augmentation method, Data Augmentation, Label information, Model architecture, mobile sensor data, neural network(NN)