Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition
This work addresses the problem of reducing computational costs for ensemble learning in human activity recognition, which is incremental as it builds on existing ensemble techniques.
The paper tackles the computational expense of deep ensemble learning for sensor-based human activity recognition by proposing Easy Ensemble, a method that implements ensemble learning in a single model with input masking for diversification. Experiments on a benchmark dataset showed its effectiveness compared to conventional methods, though no concrete performance numbers were provided.
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.