Pairwise Classifier Ensemble with Adaptive Sub-Classifiers for fMRI Pattern Analysis

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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.
Publisher
SPRINGER
Issue Date
2017-02
Language
English
Article Type
Article
Keywords

INDEPENDENT COMPONENT ANALYSIS; SUPPORT VECTOR MACHINES; FUNCTIONAL CONNECTIVITY; MULTICLASS CLASSIFICATION; FEATURE-SELECTION; RESTING-STATE; HUMAN BRAIN; MR-IMAGES; CORTEX

Citation

NEUROSCIENCE BULLETIN, v.33, no.1, pp.41 - 52

ISSN
1673-7067
DOI
10.1007/s12264-016-0077-y
URI
http://hdl.handle.net/10203/220868
Appears in Collection
EE-Journal Papers(저널논문)
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