深度强化学习的应用在多类不平衡下用于尖端分类
Suchen Li1, Zhuo Tang1, Lifang Yang1
1School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, 450001, China.
Computers in biology and medicine
|August 3, 2023
概括
ImbSorter使用深度强化学习解决了尖峰分类中的多类不平衡. 这种新的方法改善了神经发射模式的分析,即使有重叠的尖峰和杂的数据.
科学领域:
- 计算神经科学是一种神经科学.
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 尖端分类对于分析高密度微电极阵列的神经活动至关重要.
- 同时记录导致多类失衡问题,如重叠的尖峰和不同的射击速度.
研究的目的:
- 开发一个深度强化学习 (DRL) 方法,ImbSorter,用于在多类不平衡下有效的尖端分类.
- 使用动态奖励函数 (DRF) 增强对小神经元类的敏感性.
主要方法:
- 尖分类被视为马尔科夫决策过程.
- 一个包含类间不平衡比率的动态奖励函数 (DRF) 指导DRL代理.
- 对Wave_Clus和的数据集进行评估,具有重叠的尖峰和多尺度失衡.
主要成果:
- 与经典的DRL,传统的ML和先进技术相比,ImbSorter显示了更好的Macro_F1得分.
- 该方法显示了对重叠尖峰和噪声干扰的稳定性.
- 在数据集上具有偏斜的神经元发射分布的高稳定性和有前途的性能.
结论:
- 在存在显著的类不平衡的情况下,ImbSorter为尖端分类提供了一个有希望的解决方案.
- 与DRF一起的DRL方法有效地处理复杂的神经数据挑战.
- 这种方法推进了神经科学研究中神经激发模式的分析.
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