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자동 감성 인식을 위한 비교사-교사 분류기의 복합 설계

Other Titles
 Design of Hybrid Unsupervised-Supervised Classifier for Automatic Emotion Recognition 
Authors
 이지은  ;  유선국 
Citation
 Transactions of the Korean Institute of Electrical Engineers (전기학회논문지), Vol.63(9) : 1294-1299, 2014 
Journal Title
 Transactions of the Korean Institute of Electrical Engineers (전기학회논문지) 
ISSN
 1975-8359 
Issue Date
2014
Keywords
Emotion ; Physiological Signal ; K-means ; Genetic Algorithm ; SVM
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%).
Files in This Item:
T201403270.pdf Download
DOI
10.5370/KIEE.2014.63.9.1294
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
Yonsei Authors
Yoo, Sun Kook(유선국) ORCID logo https://orcid.org/0000-0002-6032-4686
Lee, Jee Eun(이지은) ORCID logo https://orcid.org/0000-0002-5177-9152
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/99818
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