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Comparison of Supervised Classification Methods with Various Data Preprocessing Procedures for Activation Detection in fMRI Data

  • Mahdi Ramezani
  • Emad Fatemizadeh
Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 38)

Abstract

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.

Keywords

Support Vector Machine Linear Discriminant Analysis Classification Error fMRI Data Independent Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Biomedical Image and Signal Processing Laboratory (BiSIPL), School of Electrical EngineeringSharif University of TechnologyTehranIran

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