ABSTRACT
Recent research has shown that it is possible to classify cognitive states of human subjects based on fMRI (functional magnetic resonance imaging) data. One of the obstacles in classifying fMRI data is the problem of high dimensionality. A single fMRI snapshot consists of thousands of voxels and since a single experiment contains many fMRI snapshots, the dimensionality of an fMRI data instance easily surpasses the order of tens of thousands. So, feature selection methods become a must from both classification and running time performance points of view. To this end several feature selection methods are studied, either general or specific to fMRI data. So far, one of the best such methods, which is specific to fMRI data, is called the "active" method [9]. In this work we combine genetic algorithms with the active method in order to improve the performance of feature selection. Specifically, we first reduce the feature dimension using the active method and search for informative features in that reduced space using genetic algorithms. We achieve better or similar levels of classification performance using a much smaller number of voxels than the active method offers.
- M. B. Aberg and J. Wessberg. An evolutionary approach to the identification of informative voxel clusters for brain state discrimination. Selected Topics in Signal Processing, IEEE Journal of, 2(6):919--928, 2008.Google Scholar
- O. Boehm, D. Hardoon, and L. Manevitz. Classifying cognitive states of brain activity via one-class neural networks with feature selection by genetic algorithms. International Journal of Machine Learning and Cybernetics, 2:125--134, 2011.Google ScholarCross Ref
- D. Collins, P. Neelin, T. Peters, and A. Evans. Automatic 3d intersubject registration of mr volumetric data in standardized talairach space. Journal of Computer Assisted Tomography, 18:192--205, 1994.Google ScholarCross Ref
- C. Davatzikos, K. Ruparel, Y. Fan, D. Shen, M. Acharyya, J. Loughead, R. Gur, and D. Langleben. Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection. NeuroImage, 28(3):663--668, 2005.Google ScholarCross Ref
- E. Formisano, F. D. Martino, and G. Valente. Multivariate analysis of fmri time series: classification and regression of brain responses using machine learning. Magnetic Resonance Imaging, 26(7):921--934, 2008.Google ScholarCross Ref
- I. Guyon and A. Elisseeff. An introduction to variable and feature selection. Journal of Machine Learning Research, 3:1157--1182, mar 2003. Google ScholarDigital Library
- T. A. Keller, M. A. Just, and V. A. Stenger. Reading span and the time-course of cortical activation in sentence-picture verification. Annual Convention of the Psychonomic Society, Orlando, FL, 2001.Google Scholar
- M. Kudo and J. Sklansky. Comparison of algorithms that select features for pattern classifiers. Pattern Recognition, 33(1):25--41, 2000.Google ScholarCross Ref
- T. M. Mitchell, R. Hutchinson, R. S. Niculescu, F. Pereira, X. Wang, M. Just, and S. Newman. Learning to decode cognitive states from brain images. Machine Learning, 57:145--175, 2004. Google ScholarDigital Library
- J. Mourão-Miranda, A. L. Bokde, C. Born, H. Hampel, and M. Stetter. Classifying brain states and determining the discriminating activation patterns: Support vector machine on functional mri data. NeuroImage, 28(4):980--995, 2005.Google ScholarCross Ref
- F. Pereira, T. Mitchell, and M. Botvinick. Machine learning classifiers and fmri: A tutorial overview. NeuroImage, 45(1, Supplement 1):S199 -- S209, 2009.Google Scholar
- R. Ramirez and M. Puiggros. A genetic programming approach to feature selection and classification of instantaneous cognitive states. In M. Giacobini, editor, Applications of Evolutionary Computing, volume 4448 of Lecture Notes in Computer Science, pages 311--319. Springer Berlin/Heidelberg, 2007. Google ScholarDigital Library
- W. Siedlecki and J. Sklansky. A note on genetic algorithms for large-scale feature selection. Pattern Recognition Letters, 10(5):335--347, 1989. Google ScholarDigital Library
- D. Whitley. A genetic algorithm tutorial. Statistics and Computing, 4:65--85, 1994.Google ScholarCross Ref
- D. Zongker and A. Jain. Algorithms for feature selection: An evaluation. In Pattern Recognition, 1996., Proceedings of the 13th International Conference on, volume 2, pages 18--22, aug 1996. Google ScholarDigital Library
Index Terms
- Improving the performance of active voxel selection in the analysis of fMRI data using genetic algorithms
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