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ITS2Graph:基于图形的生成对抗学习,用于不平衡的时间序列分类.
Chang Liu1, Donghai Guan1, Weiwei Yuan1
1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
概括
本研究介绍了ITS2Graph,这是一个基于图形的新型框架,用于解决时间序列分类中的类不平衡. 该方法有效地捕捉了复杂的依赖关系,改善了少数群体在不平衡数据集中的代表性.
科学领域:
- 数据挖掘 数据挖掘
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 时间序列分类 (TSC) 是至关重要的,但受到阶级失衡的挑战.
- 传统方法在高阶依赖性方面存在困难,尤其是在稀缺的少数样本中.
- 在不平衡的TSC中改善少数阶级的代表性是一个重大挑战.
研究的目的:
- 提出ITS2Graph,一个基于图形的生成式对抗式学习框架.
- 为了利用高阶协会来进行不平衡的时间序列分类.
- 加强少数阶级在不平衡的TSC中的代表性.
主要方法:
- 使用自动编码器用于隐性时间序列表示提取.
- 构建基于对相似性的图表,用于节点分类.
- 使用图形生成器来合成少数类特征和连接.
- 训练一个图形卷积网络 (GCN) 区分器来区分真实和生成的节点.
主要成果:
- ITS2Graph有效地解决了时间序列分类中的类不平衡问题.
- 该框架成功地利用了时间序列之间的高阶关联.
- 在22个现实世界数据集上的实验结果显示,与现有算法相比,性能优越.
结论:
- ITS2Graph为不平衡的时间序列分类提供了一个强大的解决方案.
- 基于图形的生成对抗方法增强了少数群体的阶级认可.
- 该方法在各种真实世界时间序列数据上显示了显著的改进.
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