title: Pairwise Classifier Ensemble with Adaptive Sub-Classifiers for fMRI Pattern Analysis.
abstract: The multi-voxel pattern analysis technique is applied to fMRI data for classification of high-level brain functions using pattern information distributed over multiple voxels. In this paper, we propose a classifier ensemble for multiclass classification in fMRI analysis, exploiting the fact that specific neighboring voxels can contain spatial pattern information. The proposed method converts the multiclass classification to a pairwise classifier ensemble, and each pairwise classifier consists of multiple sub-classifiers using an adaptive feature set for each class-pair. Simulated and real fMRI data were used to verify the proposed method. Intra- and inter-subject analyses were performed to compare the proposed method with several well-known classifiers, including single and ensemble classifiers. The comparison results showed that the proposed method can be generally applied to multiclass classification in both simulations and real fMRI analyses.
authors.affiliation: Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.
authors.forename: Eunwoo
authors.initials: E
authors.lastname: Kim
authors.affiliation: Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea. hwpark@kaist.ac.kr.
authors.forename: HyunWook
authors.initials: H
authors.lastname: Park
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D001931:Brain Mapping; D003198:Computer Simulation; D066264:Datasets as Topic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D018709:Statistics, Nonparametric
keywords: Ensemble learning; Functional MRI; Multi-voxel pattern analysis; Pairwise classifier