对图形卷积网络的图形数据增量 在强大的心理障碍预测中使用有限和杂的标签
Jiacheng Pan1, Yihong Dong2, Daogen Jiang1
1The Information and Intelligent Engineering Department, Ningbo City College of Vocational Technology, Ningbo, China.
概括
这项研究引入了一种新的图形数据增强方法,通过解决数据噪声和稀缺性来改善精神疾病的预测. 这种方法提高了模型的稳定性和准确性,即使数据有限或不完善.
科学领域:
- 生物医学信息学
- 机器学习
- 精神病学研究
背景情况:
- 图形神经网络 (GNN) 在生物医学任务中表现出色,但在精神疾病预测中却面临着杂和稀缺的数据.
- 现有的方法缺乏有效的解决方案来应对心理疾病预测的数据挑战.
研究的目的:
- 提出图形数据增强方法以克服精神疾病预测中的数据噪声和稀缺性.
- 增强精神疾病预测GNN模型的稳定性和准确性.
主要方法:
- 使用边缘预测器来完善图形拓,加强类似节点之间的连接,并删除噪音边缘.
- 在特征空间中纳入对抗性干扰,以提高对标签噪声的模型稳定性.
- 实施了可靠的自我检查机制,用于准确的伪标签,以帮助模型培训.
主要成果:
- 拟议的图形数据增强方法在两个多模式真实精神疾病数据集上表现出卓越的性能.
- 废除研究证实了该框架中的单个成分的有效性.
- 在噪音和稀少数据条件下,该框架被证明是有效和可扩展的基于人口的疾病预测.
结论:
- 这种新的图形数据增强方法有效地解决了精神疾病预测中的数据噪声和稀缺问题.
- 该方法提高了GNN模型的性能和稳定性,为现实应用提供了可扩展的解决方案.
- 公开可用的代码有助于进一步研究和应用精神疾病的预测.
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