title: Exploring stability-based voxel selection methods in MVPA using cognitive neuroimaging data: a comprehensive study.
abstract: 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.
authors.affiliation: Binghamton University, the State University of New York, 4400 Vestal Pkwy E, Binghamton, NY, 13902, USA.
authors.forename: Miaolin
authors.initials: M
authors.lastname: Fan
authors.affiliation: Binghamton University, the State University of New York, 4400 Vestal Pkwy E, Binghamton, NY, 13902, USA. cachou@binghamton.edu.
authors.forename: Chun-An
authors.initials: CA
authors.lastname: Chou
keywords: Feature selection; Functional MRI; Multi-voxel pattern analysis; Stability