Key result
Ensemble learning achieves a 1.19 RMSE for assessing the dynamics of affective experience ratings.
Why the study?
The congruence between affective experiences and physiological changes has been a long-debated topic, presenting a persistent challenge that recent advances in measurement, data analysis, and open science aim to address.
An ensemble learning strategy combining feature engineering and late fusion achieved an overall RMSE of 1.19 in predicting affective experience ratings from physiological changes in the EPiC challenge.
May aid affective monitoring in cardiac care; leaves open prospective validation before clinical use.
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.
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Dollack et al. (2023) studied Affective experiences and physiological changes. Ensemble learning with feature engineering and late fusion was evaluated on Overall RMSE in the test set (RMSE 1.19). An ensemble learning strategy using feature engineering and late fusion yielded an overall RMSE of 1.19 in the test set for assessing the dynamics of affective experience ratings.
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