ST-CIRL:一种基于强化学习的特征选择方法,用于增强焦虑分类
Shikha Shikha1, Divyashikha Sethia2, S Indu3
1Computer Science and engineering, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi-110042, New Delhi, New Delhi, Delhi, 110042, INDIA.
Physiological measurement
|January 29, 2025
概括
这项研究引入了使用生理信号进行焦虑分类的新框架,通过高级特征选择和强化学习实现了高精度. 该方法通过改善情绪状态识别来增强人机交互 (HCI) 系统.
科学领域:
- 生理信号处理 物理信号处理
- 人与计算机的交互 (HCI)
- 机器学习用于情感计算.
背景情况:
- 有效的人与计算机交互 (HCI) 取决于从生理信号准确地解释人类情绪状态.
- 分类这些信号需要强大的特征提取和选择来区分情绪.
- 现有的方法面临着阶级不平衡的挑战,并具有冗余性.
研究的目的:
- 引入SMOTETomek相关的交互式强化学习 (ST-CIRL) 框架,以加强焦虑分类.
- 利用元描述性统计数据来改善强化学习中的国家代表性.
- 在HCI系统中优化特征选择和分类性能.
主要方法:
- 使用SMOTETomek解决阶级不平衡,并通过修剪冗余功能来减少维度.
- 采用交互式强化学习 (IRL) 与多代理合作进行信息功能选择.
- 使用和调整分类器 (随机森林,SVM,KNN,LightGBM) 使用Optuna方法.
主要成果:
- 使用LightGBM分类器,ST-CIRL框架实现了95.35%的最大精度和95.49%的F1得分.
- 与当前最先进的方法相比,提出的方法显示出更高的性能.
- 验证SMOTETomek的失衡处理和功能优化的有效性.
结论:
- ST-CIRL框架显著提高了HCI系统中焦虑分类的准确性.
- 强化学习显示了改善基于生理信号的HCI的强大潜力.
- 开发的功能优化策略对于智能系统设计是有效的.
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