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@INPROCEEDINGS{Setz_Using_Ensemble_Classifier_Systems_for_Handling_Missing_Data_in_Emotion_Recognition_from_Physiology_One_Step_Towards_a_Practical_System_2009,
     author = {Setz, Cornelia and Schumm, Johannes and Lorenz, Claudia and Arnrich, Bert and Tr{\"{o}}ster, Gerhard},
   keywords = {arnrich_physio, SEAT},
      title = {Using Ensemble Classifier Systems for Handling Missing Data in Emotion Recognition from Physiology: One Step Towards a Practical System},
  booktitle = {Proceedings of the 2009 3rd International Conference on Affective Computing and Intelligent Interaction, ACII},
       year = {2009},
      pages = {187-194},
        url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5349590&isnumber=5349257},
        doi = {10.1109/ACII.2009.5349590},
   abstract = {Previous work on emotion recognition from physiology
has not 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 film clips 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.}
}