使用层次特征优化进行跨主题情绪识别,并使用多核协作支持矢量机器进行多核协作
Lizheng Pan1, Ziqin Tang1, Shunchao Wang1
1School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, People's Republic of China.
Physiological measurement
|November 29, 2023
概括
这项研究引入了一种新的等级特征优化方法,用于使用生理信号识别情绪. 该方法在跨主体情绪识别方面实现了高精度,超过了现有的技术.
科学领域:
- 情感计算是一种情感计算.
- 人与计算机的互动.
- 生物医学信号处理
背景情况:
- 从生理信号中识别情绪是具有挑战性的,因为个体的变化.
- 准确的跨主体情绪识别需要强大的特征表示和分类.
- 现有的方法经常与生理数据的复杂性和多道性质作斗争.
研究的目的:
- 开发一种层次特征优化方法,以利用外围生理信号有效地表达情感.
- 为了提高不同学科情绪分类的表现.
- 在情感识别任务中改进现有的支持向量机 (SVM) 限制.
主要方法:
- 提出了一种分层特征优化方法,涉及稀疏学习和对单个信号特征选择的二进制搜索.
- 实现了一种改进的基于快速关联的波器,用于多通道信号功能融合优化.
- 引入了用于支持向量机 (SVM) 分类的多核函数协作策略.
主要成果:
- 在DEAP数据集上验证了拟议的方法,用于跨主体情绪识别.
- 在四种类型的情感识别中,获得了84% (组1) 和85.07% (组2) 的准确度的竞争性表现.
- 与最先进的技术相比,表现出更高的情感识别准确度.
结论:
- 提出的层次特征优化和多核SVM策略显著提高了跨主体情绪识别准确度.
- 该方法为客观和全面的情绪识别分析提供了一个新的视角.
- 这种方法有望推动情感计算和个性化人机交互领域的发展.
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