Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition

03/08/2022
by   Tatsuhito Hasegawa, et al.
0

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 input masking as a method for diversifying the input for EE. Experiments on a benchmark dataset for HAR demonstrated the effectiveness of EE and input masking and their characteristics compared with conventional ensemble learning methods.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset