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http://dx.doi.org/10.3837/tiis.2014.06.014

GA-optimized Support Vector Regression for an Improved Emotional State Estimation Model  

Ahn, Hyunchul (Graduate School of Business IT, Kookmin University)
Kim, Seongjin (Graduate School of Business IT, Kookmin University)
Kim, Jae Kyeong (School of Management, Kyunghee University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.8, no.6, 2014 , pp. 2056-2069 More about this Journal
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.
Keywords
Emotional state estimation; Genetic Algorithm; Support Vector Regression;
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Times Cited By KSCI : 7  (Citation Analysis)
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