Abstract
Human-computer intelligent interaction (HCII) is becoming more and more important in daily life, and emotion recognition is one of the important issues of HCII. In this paper, a novel emotion recognition method based on dynamic ensemble feature selection is proposed. Firstly, a feature selection algorithm is proposed based on rough set and domain-oriented data-driven data mining theory, which can get multiple reducts and candidate classifiers accordingly. Secondly, the nearest neighborhood of each unseen sample is found in a validation subset and the most accuracy classifier is selected from the candidate classifiers. In the end, the dynamically selected classifier is used to recognize each unseen sample. The proposed method is proved to be an effective and suitable method for emotion recognition according to the result of comparative experiments.
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Yang, Y., Wang, G., Kong, H. (2009). Emotion Recognition Based on Dynamic Ensemble Feature Selection. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds) Man-Machine Interactions. Advances in Intelligent and Soft Computing, vol 59. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00563-3_22
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DOI: https://doi.org/10.1007/978-3-642-00563-3_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00562-6
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