Feature selection plays a key role in multi-voxel pattern analysis because functional magnetic resonance imaging data are typically noisy, sparse, and high-dimensional. Although the conventional evaluation criterion is the classification accuracy, selecting a stable feature set that is not sensitive to the variance in dataset may provide more scientific insights. In this study, we aim to investigate the stability of feature selection methods and test the stability-based feature selection scheme on two benchmark datasets. Top-k feature selection with a ranking score of mutual information and correlation, recursive feature elimination integrated with support vector machine, and L1 and L2-norm regularizations were adapted to a bootstrapped stability selection framework, and the selected algorithms were compared based on both accuracy and stability scores. The results indicate that regularization-based methods are generally more stable in StarPlus dataset, but in Haxby dataset they failed to perform as well as others.

中文翻译：

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使用认知神经影像数据探索MVPA中基于稳定性的体素选择方法：一项全面的研究。

特征选择在多体素模式分析中起着关键作用，因为功能性磁共振成像数据通常是嘈杂，稀疏和高维的。尽管常规评估标准是分类准确性，但是选择对数据集中的方差不敏感的稳定特征集可能会提供更多的科学见解。在这项研究中，我们旨在研究特征选择方法的稳定性，并在两个基准数据集上测试基于稳定性的特征选择方案。相互信息和相关性得分最高的Top-k特征选择，与支持向量机集成的递归特征消除以及L1和L2范数正则化均适用于自举稳定性选择框架，并根据准确性和稳定性得分对所选算法进行比较。结果表明，基于正则化的方法在StarPlus数据集中通常更稳定，但是在Haxby数据集中，它们的执行效果不如其他方法。