Design of Hybrid Unsupervised-Supervised Classifier for Automatic Emotion Recognition

Abstract - The emotion is deeply affected by human behavior and cognitive process, so it is important to do research about the emotion. However, the emotion is ambiguous to clarify because of different ways of life pattern depending on each individual characteristics. To solve this problem, we use not only physiological signal for objective analysis but also hybrid unsupervised-supervised learning classifier for automatic emotion detection. The hybrid emotion classifier is composed of K-means, genetic algorithm and support vector machine. We acquire four different kinds of physiological signal including electroencephalography(EEG), electrocardiography(ECG), galvanic skin response(GSR) and skin temperature(SKT) as well as we use 15 features extracted to be used for hybrid emotion classifier. As a result, hybrid emotion classifier(80.6%) shows better performance than SVM(31.3%).Key Words : Emotion, Physiological Signal, K-means, Genetic Algorithm, SVM†Corresponding Author : Dept. of Medical Engineering, Yonsei University College of Medicine, Korea.E-mail: sunkyoo@yuhs.ac*Graduate School of Biomedical Engineering, Yonsei University, Korea.Received : May 23, 2014; Accepted : August 21, 2014그림1실험 프로토콜Fig. 1Experiment Protocol

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