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.
Keywords
This research was supported by the brain Neuroinformatics research Program by Ministry of Commerce,Industry and Energy.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ververidis, D., Kotropoulos, C.: Emotional Speech Classification Using Gaussian Mixture Models. In: IEEE International Symposium on Circuits and Systems (ISCAS 2005), Kobe, Japan, pp. 2871–2874 (2005)
Lee, C.M., Narayanan, S.S.: Toward Detecting Emotions in Spoken Dialogs. Toward Detecting Emotions in Spoken Dialogs 13, 293–303
Wagner, J., Kim, J.H., Andre, E.: From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification. In: Proceedings of IEEE International Conference on Multimedia and Expo. (ICME 2005), Amsterdam, pp. 940–943 (2005)
Pudil, P., Novovicova, J.: Novel Methods for Subset Selection with Respect to Problem knowledge. IEEE Intelligent Systems 13(2), 66–74 (1998)
Lin, Y.L., Gang, W.: Speech Emotion Recognition Based on HMM and SVM. Proceedings of Machine Learning and Cybernetics 8, 4898–4901 (2005)
Morchen, F., Ultsch, A., Thies, M., Lohken, I.: Modeling Timbre Distance With Temporal Statistics From Polyphonic Music. IEEE Transaction on Audio, Speech and Language Processing 14(1), 81–90 (2006)
Combarro, E.F., Montanes, E., Diaz, I., Ranilla, J., Mones, R.: Introducing a Family of Linear Measures for Feature Selection in Text Categorization. IEEE Transactions on Knowledge and Data Engineering 17(9), 1223–1232 (2005)
John, G.H., Kohavi, R., Pfleger, K.: Irrelevant Features and The Subset Selection Problem. In: John, G.H., Kohavi, R., Pfleger, K. (eds.) Proceedings of the Eleventh International Conference on Machine learning, New Brunswick, NJ, pp. 121–129 (1994)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A bradford book, London (1998)
Park, C.H., Sim, K.B.: The Implementation of The Emotion Recognition from Speech and Facial Expression System. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3611, pp. 85–88. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Park, CH., Sim, KB. (2006). The Novel Feature Selection Method Based on Emotion Recognition System. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence and Bioinformatics. ICIC 2006. Lecture Notes in Computer Science(), vol 4115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816102_77
Download citation
DOI: https://doi.org/10.1007/11816102_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37277-6
Online ISBN: 978-3-540-37282-0
eBook Packages: Computer ScienceComputer Science (R0)