Emotion Feature Selection from Physiological Signal Based on BPSO

DOI

10.2991/iske.2007.130How to use a DOI?

Keywords

Feature Selection, Binary Particle Swarm Optimization(BPSO), Physiological Signals, Emotion Recognition.

Abstract

In emotion recognition, many irrelevant and redundant features will affect recognition results, so feature selection is necessary. Aimed at emotion physiological signal feature selection, this paper proposed with improved discrete binary particle swarm optimization(BPSO) to increase the correct classification rate of emotion state. When recognizing four emotional states with nearest classifier by four physiological signals, the whole correct recognition rate is up to 85%. Experimental results demonstrate that the BPSO is an effective way to emotion physiological signals feature selection.

Copyright

© 2007, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ruiqing Yang
AU  - Guangyuan Liu
PY  - 2007/10
DA  - 2007/10
TI  - Emotion Feature Selection from Physiological Signal Based on BPSO
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 760
EP  - 763
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.130
DO  - 10.2991/iske.2007.130
ID  - Yang2007/10
ER  -