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

Mahdi Ramezani () and Emad Fatemizadeh ()
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Mahdi Ramezani: Sharif University of Technology
Emad Fatemizadeh: Sharif University of Technology

Chapter Chapter 5 in Computational Neuroscience, 2010, pp 75-83 from Springer

Abstract: 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 (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/978-0-387-88630-5_5

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