Emotion feature selection from physiological signal based on BPSO

被引:2
作者
Yang, Ruiqing [1 ]
Liu, Guangyuan [1 ]
机构
[1] SW Univ, Sch Informat & Comp Sci, Chongqing 400715, Peoples R China
来源
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND KNOWLEDGE ENGINEERING (ISKE 2007) | 2007年
关键词
feature selection; binary particle swarm optimization(BPSO); physiological signals; emotion recognition;
D O I
10.2991/iske.2007.130
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页数:1
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