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

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• 作者：Eunwoo Kim ; HyunWook Park
• 关键词：Ensemble learning ; Functional MRI ; Multi ; voxel pattern analysis ; Pairwise classifier
• 刊名：Neuroscience Bulletin
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：33
• 期：1
• 页码：41-52
• 全文大小：
• 刊物主题：Neurosciences; Human Physiology; Anesthesiology; Anatomy; Neurology; Pain Medicine;
• 出版者：Springer Singapore
• ISSN：1995-8218
• 卷排序：33

文摘

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.