会议专题

Emotion Feature Selection from Physiological Signal Based on BPSO

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.

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

Ruiqing Yang Guangyuan Liu

School of Computer & Information Science, Southwest University, Chongqing 400715, P. R. China School of Electronic Information Engineering, Southwest University, Chongqing 400715, P. R. China

国际会议

The 2007 International Conference on Intelligent Systems and Knowledge Engineering(第二届智能系统与知识工程国际会议)

成都

英文

1467-1470

2007-10-15(万方平台首次上网日期,不代表论文的发表时间)