تقرير
Ensemble Learning to Assess Dynamics of Affective Experience Ratings and Physiological Change
| العنوان: | Ensemble Learning to Assess Dynamics of Affective Experience Ratings and Physiological Change |
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| المؤلفون: | Dollack, Felix, Kiyokawa, Kiyoshi, Liu, Huakun, Perusquia-Hernandez, Monica, Raman, Chirag, Uchiyama, Hideaki, Wei, Xin |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning |
| الوصف: | 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. Comment: This manuscript is to be published in the 2023 11th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) proceedings |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2312.16036Test |
| رقم الانضمام: | edsarx.2312.16036 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |