粗糙集二进制布谷鸟算法在情感识别中的应用

金纯; 陈光勇

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粗糙集二进制布谷鸟算法在情感识别中的应用

更新时间：2026-09-04

- 粗糙集二进制布谷鸟算法在情感识别中的应用
- Application of Binary Cuckoo Algorithm Based on Rough Set in Emotion Recognition
- 微电子学与计算机 2018年35卷第3期页码：37-41
- 作者机构：
  
  1. 重庆邮电大学机构无线移动通信理论与技术重点实验室,重庆,400065
  2. 重庆金瓯科技发展有限责任公司,重庆,400041
- 作者简介：
- 基金信息：
  
  重庆市重点产业共性关键技术创新专项:物联网智能硬件模组 cstr2015zdcy-ztzx4008
- DOI：
  中图分类号：
- 纸质出版：2018
- 稿件说明：
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金纯, 陈光勇. 粗糙集二进制布谷鸟算法在情感识别中的应用[J]. 微电子学与计算机, 2018, 35(3): 37-41.

JIN Chun, CHEN Guang-yong. Application of Binary Cuckoo Algorithm Based on Rough Set in Emotion Recognition[J]. 2018, 35(3): 37-41.
金纯, 陈光勇. 粗糙集二进制布谷鸟算法在情感识别中的应用[J]. 微电子学与计算机, 2018, 35(3): 37-41. DOI：

JIN Chun, CHEN Guang-yong. Application of Binary Cuckoo Algorithm Based on Rough Set in Emotion Recognition[J]. 2018, 35(3): 37-41. DOI：

摘要

为了提高情感识别过程中选择最优情感特征子集的能力

提出了一种粗糙集二进制布谷鸟算法.首先分别提取皮肤电、呼吸、肌电、脑电四种生理信号的原始特征; 然后使用粗糙集二进制布谷鸟算法进行特征的优化选择

并使用支持向量机进行情感分类.仿真分析表明: 提出的算法较好地优化了特征选择过程

可以通过较少特征获得较高的识别率

也说明了多模态生理信号的情感识别效果要优于单模态生理信号.

Abstract

In order to improve the ability of selecting the best subset of emotion features in emotion recognition

a rough set binary cuckoo algorithm is proposed. The original features of four physiological signals which are galvanic skin reaction

respiratory

electromyography

electroencephalogram are extracted; then

the rough set binary cuckoo algorithm is used to optimize the feature selection

and the support vector machine is used to classify the emotions. Simulation results show that the proposed algorithm can optimize the feature selection process and achieve higher recognition rate with fewer features. It also shows that the emotion recognition effect of multimodal physiological signals is better than that of single modality physiological signal.

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references

Koelstra S, Muhl C, Soleymani M, et al. DEAP: a database for emotion analysis; using physiological signals[J]. IEEE Transactions on Affective Computing, 2012, 3(1): 18-31. DOI: 18

Yang X S, Deb S. Cuckoo search via lévy flights[C]// Nature % Biologically Inspired Computing, NaBIC 2009. Springer Verlag, Germany, World Congress on, 2009: 210-214.

Yusuf R, Tanev I, Shimohara K. Application of genetic programming and genetic algorithm in evolving emotion recognition module[C]// Evolutionary Computation. Sendai, Japan, IEEE, 2015: 1444-1449.

Wang X, Yang J, Teng X, et al. Feature selection based on rough sets and particle swarm optimization[J]. Pattern Recognition Letters, 2007, 28(4): 459-471. DOI: 459

Pawlak Z. Rough sets[J]. International Journal of Parallel Programming, 1982, 38(5): 88-95. DOI: 88

Rodrigues D, Pereira L A M, Almeida T N S, et al. BCS: a binary cuckoo search algorithm for feature selection[C]// IEEE International Symposium on Circuits and Systems. Beijing, IEEE, 2013: 465-468.

Alzoubi O, Sidney K D, Calvo R A. Detecting naturalistic expressions of nonbasic affect using physiological signals[J]. Affective Computing IEEE Transactions on, 2012, 3(3): 298-310. DOI: 298

Schröder M. A circumplex model of affect[J]. Personality and Social Psychology, 1980(39): 1161-1178. DOI: 1161

Cheng Z, Tao H, Zhang X, et al. A novel speech emotion recognition algorithm based on combination of emotion data field and ant colony search strategy[J]. 东南大学学报(英文版), 2016, 32(2): 158-163. DOI: 158

Yang X S, Deb S. Engineering optimisation by cuckoo search[J]. International Journal of Mathematical Modelling % Numerical Optimisation, 2010, 1(4): 330-343. DOI: 330

Picard R W, Vyzas E, Healey J. Toward machine emotional intelligence: analysis of affective physiological state[J]. IEEE Transactions on Pattern Analysis % Machine Intelligence, 2001, 23(10): 1175 - 1191. DOI: 1175

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