The novel feature selection algorithm for emotion recognition
- Authors
- Kim, H.-D.; Cho, Y.-I.; Sim, K.-B.
- Issue Date
- Jan-2007
- Keywords
- Emotion recognition; Feature selection; GAFS; IFS; Reinforcement learning; SFS
- Citation
- Proceedings of the 12th International Symposium on Artificial Life and Robotis, AROB 12th'07, pp 740 - 743
- Pages
- 4
- Journal Title
- Proceedings of the 12th International Symposium on Artificial Life and Robotis, AROB 12th'07
- Start Page
- 740
- End Page
- 743
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/55172
- ISSN
- 0000-0000
- Abstract
- This paper presents an original feature selection method for Emotion Recognition which includes many original elements. Feature selection has some merit regarding pattern recognition performance. Thus, we developed a method called an 'Interactive Feature Selection' and the results (selected features) of the IFS were applied to an emotion recognition system (ERS), which was also implemented in this research. Our innovative feature selection method was based on a Reinforcement Learning Algorithm and since it required responses from human users, it was denoted an 'Interactive Feature Selection (IFS)'. By performing the IFS, we were able to obtain three top features and apply them to the ERS. Comparing those results from a random selection and Sequential Forward Selection (SFS) and Genetic Algorithm Feature Selection (GAFS), we verified that the top three features were better than the randomly selected feature set. ©ISAROB 2007.
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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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