Using ensemble classifier systems for handling missing data in emotion recognition from physiology: One step towards a practical system

Previous work on emotion recognition from physiology has rarely addressed the problem of missing data. However, data loss due to artifacts is a frequent phenomenon in practical applications. Discarding the whole data instance if only a part is corrupted results in a substantial loss of data. To address this problem, two methods for handling missing data (imputation and reduced-feature models) in combination with two classifier fusion approaches (majority and confidence voting) are investigated in this work. The five emotions amusement, anger, contentment, neutral and sadness were elicited in 20 subjects by films while six physiological signals (ECG, EMG, EOG, EDA, respiration and finger temperature) were recorded. Results show that classifier fusion significantly increases the recognition accuracy in comparison to single classifiers by up to 16.3%. Regarding the methods for handling missing data, reduced-feature models are competitive or even slightly better than models which employ imputation. This is beneficial for practical applications where computational complexity is critical.