An Ensemble Framework Based on Fine Multi-Window Feature Engineering and Overfitting Prevention for Transportation Mode Recognition

This paper presents our solution to the SHL recognition challenge 2023 which focuses on recognizing 8 transportation modes in a user-independent manner based on motion and GPS sensor data. Our team ZZL propose an ensemble framework based on fine multi-window feature engineering and overfitting prevention. Firstly, we extracted a large and diverse set of features in the feature engineering process, including incorporating OpenStreetMap data to better leverage location data, and introducing multiple time windows to extract long, medium, and short term aggregated features, providing rich feature inputs. Secondly, we proposed an ensemble framework that comprehensively utilizes different techniques to prevent overfitting, including data downsampling, fine-tuning data distribution, designed train-test splitting, and model integration. Moreover, we applied post-processing on the model predictions to smooth the predicted results. Finally, we achieve F1-score of 0.868 on validation dataset.