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从功能性大脑连接性进行性别分类:对多个数据集的概括性性别分类器的概括性

Lisa Wiersch^1,2, Patrick Friedrich^1,2, Sami Hamdan^1,2

¹Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.

bioRxiv : the preprint server for biology

|September 11, 2023

概括

使用大型多样化的训练数据集可以提高机器学习模型在神经成像中的通用性. 对来自多个来源的综合数据的训练产生了最佳的性别分类性能,优于单个数据集模型.

科学领域:

神经科学是一个神经科学.
机器学习机器学习
数据科学数据科学数据科学

背景情况:

神经科学中的机器学习 (ML) 模型通常由于小型训练数据集而受到有限的概括性影响.
优化训练样本组成对于提高在未见神经成像数据上的ML模型性能至关重要.

研究的目的:

使用神经成像数据系统地研究训练样本组成对性别分类模型概括性能的影响.
为了比较单个数据集与复合数据集训练样本的有效性,用于ML模型.

主要方法:

从单个数据集或四个数据集的复合样本中训练有素的性别分类模型.
使用平均跨样本分类准确度和包裹分类空间一致性的量化概括性能.

主要成果:

当模型在单个数据集上进行训练时,泛化性能显著变化,一些数据集在交叉验证中显示出更好的"匹配".
在复合样本上训练的模型在所有测试样本中始终实现了最高的概括性能.
复合训练模型甚至可以很好地概括到不包括在训练集中的数据集.

结论:

一个庞大而异质的训练样本,包括来自多个不同的数据集的数据,对于在神经成像ML中取得强大而可概括的结果至关重要.
来自各种来源的数据的结合显著提高了应用到神经成像的ML模型的预测能力和可靠性.

关键词:

大数据就是大数据.可以概括的概括性.机器学习是机器学习.神经成像是一种神经成像.静止状态的功能连接.性别分类性别分类

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