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Comparison of supervised classification methods with various data preprocessing procedures for activation detection in fMRI data

Ramezani, M ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1007/978-0-387-88630-5_5
  3. Abstract:
  4. In this study we compare five classification methods for detecting activation in fMRI data: Fisher linear discriminant, support vector machine, Gaussian nave Bayes, correlation analysis and k-nearest neighbor classifier. In order to enhance classifiers performance a variety of data preprocessing steps were employed. The results show that although kNN and linear SVM can classify active and nonactive voxels with less than 1.2% error, careful preprocessing of the data, including dimensionality reduction, outlier elimination, and denoising are important factors in overall classification
  5. Keywords:
  7. Source: Springer Optimization and Its Applications ; Volume 38 , 2010 , Pages 75-83 ; 19316828 (ISSN)
  8. URL: http://link.springer.com/chapter/10.1007%2F978-0-387-88630-5_5