Ensemble Learning to Assess Dynamics of Affective Experience Ratings and Physiological Change
This work addresses the long-standing problem in psychology and neuroscience of linking emotions to physiological data, but it is incremental as it builds on existing methods for a specific data challenge.
The paper tackled the challenge of aligning affective experiences with physiological changes by proposing an ensemble learning approach, achieving an overall RMSE of 1.19 on the test set.
The congruence between affective experiences and physiological changes has been a debated topic for centuries. Recent technological advances in measurement and data analysis provide hope to solve this epic challenge. Open science and open data practices, together with data analysis challenges open to the academic community, are also promising tools for solving this problem. In this entry to the Emotion Physiology and Experience Collaboration (EPiC) challenge, we propose a data analysis solution that combines theoretical assumptions with data-driven methodologies. We used feature engineering and ensemble selection. Each predictor was trained on subsets of the training data that would maximize the information available for training. Late fusion was used with an averaging step. We chose to average considering a ``wisdom of crowds'' strategy. This strategy yielded an overall RMSE of 1.19 in the test set. Future work should carefully explore if our assumptions are correct and the potential of weighted fusion.