Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data.
Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data.
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SVM方法结合体素选择对fMRI数据对象类别分类的比较研究
DOI：
10.1371/journal.pone.0017191
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发表时间：
2011-02-16
期刊：
影响因子：
3.7
通讯作者：
Yao L
中科院分区：
文献类型：
--
作者：
Song S;Zhan Z;Long Z;Zhang J;Yao L
背景支持向量机作为一种准确可靠的方法被广泛应用于从功能磁共振成像(FMRI)数据中破译大脑模式。以前的研究没有发现非线性(多项式核)支持向量机与线性支持向量机相比有明显的好处。本文将径向基核的非线性支持向量机与线性支持向量机进行了比较。不同于传统的只关注不同类型的支持向量机或体素选择方法的研究，本文旨在研究线性支持向量机和径向基函数支持向量机在fMRI分类中的整体性能，以及体素选择方法在分类精度和时间上的性能。方法/主要结果采用6种不同的体素选择方法来确定在分类4类对象时，线性核和径向基函数核的支持向量机分类器将包括fMRI数据的哪些体素。然后比较了体素选择和分类方法的整体性能。结果表明：(1)体素选择对分类器的分类精度有重要影响：在相对低维的特征空间，RBF支持向量机明显优于线性支持向量机；在相对高维的特征空间，线性支持向量机的性能优于线性支持向量机；(2)综合考虑分类精度和耗时，以相对较多的体素作为特征的线性支持向量机和具有较小体素集的径向基函数支持向量机(在PCA之后)可以获得更好的精度和更短的时间。结论/意义本工作首次将线性支持向量机和径向基函数支持向量机结合体素选择方法用于fMRI数据分类。根据研究结果，如果只考虑分类精度，适当小体素的RBF支持向量机和相对较多体素的线性支持向量机是两种建议的解决方案；如果用户更关心计算时间，保留部分主成分作为特征的相对较小的体素集的RBF支持向量机是更好的选择。
Background Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming. Methodology/Principal Findings Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time. Conclusions/Significance The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice.
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影响因子：
5.7
作者：
Pereira F;Mitchell T;Botvinick M
通讯作者：
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影响因子：
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作者：
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作者：
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发表时间：
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影响因子：
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