Research article
Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification algorithms
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* Corresponding author: Raji Balasubramanian rbalasub@schoolph.umass.edu
1 BG Medicine, Inc., 610 Lincoln St., Waltham, MA 02451, USA
2 Institute for Bioinformatics and Translational Research, UMIT, Eduard Wallnoefer Zentrum 1, 6060 Hall in Tyrol, Austria
3 Optimal Medicine Ltd., Warwick Enterprise Park, Wellesbourne, Warwick CV35 9EF, UK
4 Division of Biostatistics and Epidemiology, University of Massachusetts - Amherst, 715 North Pleasant Street, Amherst, MA 01003, USA
BMC Bioinformatics 2010, 11:447 doi:10.1186/1471-2105-11-447
Published: 3 September 2010Additional files
Additional file 1:
Simulation Results - Supplement. This file includes additional simulation results for the following settings: (i) Comparison of statistical power and average classification accuracy for classifiers KNN, PAM, RF and SVM, when n = 50; (ii) Estimates of average classification accuracy for classifiers KNN, PAM, RF and SVM, based on independent test sets.
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