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

• 来源期刊: 神经科学通报（英文版）
• 年，卷（期）: 2017,（1）
• 页码范围: 41-52
• 页数: 12页
• 语种: 英文
• DOI: 10.1007/s12264-016-0077-y

摘要

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.