Exploring stability-based voxel selection methods in MVPA using cognitive neuroimaging data: a comprehensive study

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• 作者：Miaolin Fan ; Chun-An Chou
• 刊名：Brain Informatics
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：3
• 期：3
• 页码：193-203
• 全文大小：1,649 KB
• 刊物类别：Artificial Intelligence (incl. Robotics); Health Informatics; Neurosciences; Computation by Abstract
• 刊物主题：Artificial Intelligence (incl. Robotics); Health Informatics; Neurosciences; Computation by Abstract Devices; Cognitive Psychology;
• 出版者：Springer Berlin Heidelberg
• ISSN：2198-4026
• 卷排序：3

文摘

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.KeywordsFeature selectionStabilityFunctional MRIMulti-voxel pattern analysis