Ensemble Learning to Assess Dynamics of Affective Experience Ratings and
  Physiological Change
Felix Dollack Kiyoshi Kiyokawa Huakun Liu Monica Perusquia-Hernandez Chirag Raman Hideaki Uchiyama Xin Wei
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.