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Comparative Study of SVM Methods Combined with Voxel Selection for Object Category Classification on fMRI Data

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  • Sutao Song
  • Zhichao Zhan
  • Zhiying Long
  • Jiacai Zhang
  • Li Yao

Abstract

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.

Suggested Citation

  • Sutao Song & Zhichao Zhan & Zhiying Long & Jiacai Zhang & Li Yao, 2011. "Comparative Study of SVM Methods Combined with Voxel Selection for Object Category Classification on fMRI Data," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-11, February.
  • Handle: RePEc:plo:pone00:0017191
    DOI: 10.1371/journal.pone.0017191
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    References listed on IDEAS

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    1. Warren Torgerson, 1952. "Multidimensional scaling: I. Theory and method," Psychometrika, Springer;The Psychometric Society, vol. 17(4), pages 401-419, December.
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    1. Zhiying Long & Yubao Wang & Xuanping Liu & Li Yao, 2019. "Two-step paretial least square regression classifiers in brain-state decoding using functional magnetic resonance imaging," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-16, April.
    2. Skripnikov, A. & Michailidis, G., 2019. "Joint estimation of multiple network Granger causal models," Econometrics and Statistics, Elsevier, vol. 10(C), pages 120-133.
    3. Skripnikov, A. & Michailidis, G., 2019. "Regularized joint estimation of related vector autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 164-177.
    4. Meng Sha & Hua Yang & Jianwei Wu & Jianning Qi, 2025. "Study on Intelligent Classing of Public Welfare Forestland in Kunyu City," Land, MDPI, vol. 14(1), pages 1-15, January.
    5. Danial Jahed Armaghani & Panagiotis G. Asteris & Behnam Askarian & Mahdi Hasanipanah & Reza Tarinejad & Van Van Huynh, 2020. "Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness," Sustainability, MDPI, vol. 12(6), pages 1-17, March.
    6. Maelaynayn El baida & Farid Boushaba & Mimoun Chourak & Mohamed Hosni & Hichame Sabar, 2024. "Classification machine learning models for urban flood hazard mapping: case study of Zaio, NE Morocco," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(11), pages 10013-10041, September.

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