TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
GA-optimized Support Vector Regression for an Improved Emotional State Estimation Model |
| 영문제목(English Title) |
GA-optimized Support Vector Regression for an Improved Emotional State Estimation Model |
| 저자(Author) |
Hyunchul Ahn
Seongjin Kim
Jae Kyeong Kim
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| 원문수록처(Citation) |
VOL 08 NO. 06 PP. 2056 ~ 2069 (2014. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
In order to implement interactive and personalized Web services properly, it is necessary to understand the tangible and intangible responses of the users and to recognize their emotional states. Recently, some studies have attempted to build emotional state estimation models based on facial expressions. Most of these studies have applied multiple regression analysis (MRA), artificial neural network (ANN), and support vector regression (SVR) as the prediction algorithm, but the prediction accuracies have been relatively low. In order to improve the prediction performance of the emotion prediction model, we propose a novel SVR model that is optimized using a genetic algorithm (GA). Our proposed algorithm—GASVR—is designed to optimize the kernel parameters and the feature subsets of SVRs in order to predict the levels of two aspects—valence and arousal—of the emotions of the users. In order to validate the usefulness of GASVR, we collected a real-world data set of facial responses and emotional states via a survey. We applied GASVR and other algorithms including MRA, ANN, and conventional SVR to the data set. Finally, we found that GASVR outperformed all of the comparative algorithms in the prediction of the valence and arousal levels.
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| 키워드(Keyword) |
Emotional state estimation
Genetic Algorithm
Support Vector Regression
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| 파일첨부 |
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