A genetic programming approach to feature selection and classification of instantaneous cognitive states

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4448 LNCS, Issue , 2007, Pages 311-319

  Ramirez, Rafael ^a   Puiggros, Montserrat ^a  

^a UNIVERSITAT POMPEU FABRA   (Spain)

Author keywords

Feature extraction; fMRI data; Genetic programming

Indexed keywords

CLASSIFICATION (OF INFORMATION); COGNITIVE SYSTEMS; FEATURE EXTRACTION; MAGNETIC RESONANCE IMAGING; PROBLEM SOLVING;

AUDITORY STIMULI; COGNITIVE STATES; FMRI DATA;

GENETIC PROGRAMMING;

EID: 34548060141     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-71805-5_34     Document Type: Conference Paper

References (18)

1. Haxby, J. et al. (2001) Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex. Science 2001, 293:2425-2430.
2. Wang, X., et al. (2003) Training fMRI Classifiers to Detect Cognitive States across Multiple Human Subjects. Neural Information Processing Systems.
3. Mitchell, T.M., Hutchinson, R., Niculescu, R.S., Pereira, F., Wang, X., Just, M. and Newman S. (2004). Learning to Decode Cognitive States from Brain Images, Machine Learning, Vol. 57, Issue 1-2, pp. 145-175.
4. Mitchell, T., Hutchinson, R., Just, M., Niculescu, R.N., Pereira, F., Wang, X. (2003). Classifying Instantaneous Cognitive States from fMRI Data, American Medical Informatics Association Symposium, October 2003.
5. Cox, D. D., Savoy, R. L. (2003). Functional magnetic resonance imaging (fMRI) "brain reading" : Detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage, 19, 261-270.
6. M. Dash and H. Liu, (1997). Feature selection for classification, Intell. Data Anal., vol. 1, no. 3, pp. 131-156.
7. A. L. Blum and P. Langley, (1997). Selection of relevant features and examples in machine learning, Artif. Intell. Special Issue on Relevance, vol. 97, pp. 245-271.
8. W. Siedlecki and J. Sklansky, (1989). A note on genetic algorithms for largescale feature selection, Patt. Recognit. Lett., vol. 10, pp. 335-347.
9. J. Casillas, O. Cordon, M. J. Del Jesus, and F. Herrera, Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems, Inform. Sci., vol. 136, pp. 135-157, 2001.
10. N. R. Pal, S. Nandi, and M. K. Kundu, (1998). Self-crossover: A new genetic operator and its application to feature selection, Int. J. Syst. Sci., vol. 29, no. 2, pp. 207-212.
11. J. Sherrah, R. E. Bogner, and A. Bouzerdoum, (1996). Automatic selection of features for classification using genetic programming, in Proc. Australian New Zealand Conf. Intelligent Information Systems, pp. 284-287.
12. W. Siedlecki and J. Sklansky, (1988). On automatic feature selection, Int. J. Pattern Recognit. Artif. Intell., vol. 2, no. 2, pp. 197-220.
13. M. Kudo and J. Sklansky, Comparison of algorithms that select features for pattern classifiers, Patt. Recognit., vol. 33, pp. 25-41, 2000.
14. M. Richeldi and P. Lanzi, (1996). Performing effective feature selection by investigating the deep structure of the data, in Proc. 2nd Int. Conf. Knowledge Discovery and Data Mining. Menlo Park, CA, pp. 379-383.
15. J. R. Koza, (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press.
16. W. Banzhaf, P. Nordin, R. E. Keller, and F. D. Francone, Genetic Programming: An Introduction. New York: Morgan Kaufmann.
17. Durga Prasad Muni, Nikhil R. Pal, and Jyotirmoy Das, (2006). Genetic Programming for Simultaneous Feature Selection and Classifier Design IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics, vol.36-1.
18. Wessinger, C.M., VanMeter, J., Tian, B., Van Lare, J., Pekar, J., Rauschecker, J.P. (2001). Hierarchical organization of the human auditory cortex revealed by functional magnetic resonance imaging. J Cogn Neurosci. 2001 Jan 1;13(1):1-7.