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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2203.04153 |
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| _version_ | 1866914506535337984 |
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| author | Hasegawa, Tatsuhito Kondo, Kazuma |
| author_facet | Hasegawa, Tatsuhito Kondo, Kazuma |
| contents | Sensor-based human activity recognition (HAR) is a paramount technology in the Internet of Things services. HAR using representation learning, which automatically learns a feature representation from raw data, is the mainstream method because it is difficult to interpret relevant information from raw sensor data to design meaningful features. Ensemble learning is a robust approach to improve generalization performance; however, deep ensemble learning requires various procedures, such as data partitioning and training multiple models, which are time-consuming and computationally expensive. In this study, we propose Easy Ensemble (EE) for HAR, which enables the easy implementation of deep ensemble learning in a single model. In addition, we propose various techniques (input variationer, stepwise ensemble, and channel shuffle) for the EE. Experiments on a benchmark dataset for HAR demonstrated the effectiveness of EE and various techniques and their characteristics compared with conventional ensemble learning methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2203_04153 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition Hasegawa, Tatsuhito Kondo, Kazuma Computer Vision and Pattern Recognition Sensor-based human activity recognition (HAR) is a paramount technology in the Internet of Things services. HAR using representation learning, which automatically learns a feature representation from raw data, is the mainstream method because it is difficult to interpret relevant information from raw sensor data to design meaningful features. Ensemble learning is a robust approach to improve generalization performance; however, deep ensemble learning requires various procedures, such as data partitioning and training multiple models, which are time-consuming and computationally expensive. In this study, we propose Easy Ensemble (EE) for HAR, which enables the easy implementation of deep ensemble learning in a single model. In addition, we propose various techniques (input variationer, stepwise ensemble, and channel shuffle) for the EE. Experiments on a benchmark dataset for HAR demonstrated the effectiveness of EE and various techniques and their characteristics compared with conventional ensemble learning methods. |
| title | Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2203.04153 |