The novel feature selection method based on emotion recognition system
- Authors
- Park, Chang-Hyun; Sim, Kwee-Bo
- Issue Date
- 2006
- Publisher
- SPRINGER-VERLAG BERLIN
- Keywords
- reinforcement learning; feature selection; emotion recognition; SFS; GAFS
- Citation
- COMPUTATIONAL INTELLIGENCE AND BIOINFORMATICS, PT 3, PROCEEDINGS, v.4115, pp 731 - 740
- Pages
- 10
- Journal Title
- COMPUTATIONAL INTELLIGENCE AND BIOINFORMATICS, PT 3, PROCEEDINGS
- Volume
- 4115
- Start Page
- 731
- End Page
- 740
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/52690
- DOI
- 10.1007/11816102_77
- ISSN
- 0302-9743
1611-3349
- 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'. By performing an 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.
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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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