Classifier ensembles for fMRI data analysis: an experiment

Magnetic Resonance Imaging

Volumn 28, Issue 4, 2010, Pages 583-593

  Kuncheva, Ludmila I ^a   Rodríguez, Juan J ^b  

^a BANGOR UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF BURGOS   (Spain)

Author keywords

Classifier ensembles; FMRI; Pattern recognition; Random oracle; Random subspace; Rotation forest

Indexed keywords

ACCURACY; ARTICLE; AUTOMATED PATTERN RECOGNITION; BRAIN COMPUTER INTERFACE; CLASSIFICATION; CLASSIFIER; CONTROLLED STUDY; DATA ANALYSIS; EXPERIMENT; FUNCTIONAL MAGNETIC RESONANCE IMAGING; IMAGE ANALYSIS; PRIORITY JOURNAL; RELIABILITY; SUPPORT VECTOR MACHINE; VISUAL STIMULATION;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; BRAIN; BRAIN MAPPING; COMPUTER SIMULATION; COMPUTERS; HUMANS; IMAGE ENHANCEMENT; MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SIGNAL PROCESSING, COMPUTER-ASSISTED; STATISTICS AS TOPIC;

EID: 77952293118     PISSN: 0730725X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.mri.2009.12.021     Document Type: Article

References (58)

1. Norman K.A., Polyn A.M., Detre G.J., Haxby J.V. Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn Sci 2006, 10:424-430.
2. Haxby J.V., Gobbini M., Furey M.L., Ishal A., Schouten J.L., Pietrini P. Distributed and overlapping representation of faces and objects in ventral temporal cortex. Science 2001, 293:2425-2430.
3. Kay K.N., Naselaris T., Prenger R.J., Gallant J.L. Identifying natural images from human brain activity. Nat Lett 2008, 452:352-355.
4. Haynes J.-D., Sakai K., Rees G., Gilbert S., Frith C., Passingham R.E. Reading hidden intentions in the human brain. Curr Biol 2007, 17:323-328.
5. Hardoon D.R., Mourao-Miranda J., Brammer M., Shawe-Taylor J. Unsupervised analysis of fMRI data using kernel canonical correlation. NeuroImage 2007, 37(4):1250-1259.
6. Pereira F., Mitchell T., Botvinick M. Machine learning classifiers and fMRI: a tutorial overview. NeuroImage 2009, 45(1, Supplement 1):S199-S209.
7. Liang L.C., Cherkassky V., Rottenberg D.A. Spatial SVM for feature selection and fMRI activation detection. Proceedings of the IEEE International Joint Conference on Neural Networks, Vancouver, Canada 2006, 1463-1469.
8. De Martino F., Valente G., Staeren N., Ashburner J., Goebel R., Formisano E. Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. NeuroImage 2008, 43(1):44-58.
9. Statistical Parametric Mapping: The Analysis of Functional Brain Images 2007, 465-469. Academic Press, London. K. Friston, J. Ashburner, S. Kiebel, T. Nichols, W. Penny (Eds.).
10. Clithero J.A., Carter R.M., Huettel S.A. Local pattern classification differentiates processes of economic valuation. NeuroImage 2009, 45(4):1329-1338.
11. Kamitani Y., Tong F. Decoding the visual and subjective contents of the human brain. Nat Neurosci 2005, 8:679-685.
12. Mitchell T., Hutchinson R., Just M., Niculescu R., Pereira F., Wang X. Classifying instantaneous cognitive states from fMRI data. American Medical Informatics Association Symposium 2003, 465-469.
13. Weiskopf N., Scharnowski F., Veit R., Goebel R., Birbaumer N., Mathiak K. Self-regulation of local brain activity using real-time functional magnetic resonance imaging (fMRI). J Physiol 2004, 98(4-6):357-373.
14. Weiskopf N., Sitaramb R., Josephsa O., Veitb R., Scharnowskid F., Goebele R., et al. Real-time functional magnetic resonance imaging: methods and applications. Magn Reson Imaging 2007, 25:989-1003.
15. Cox D.D., Savoy R.L. Functional magnetic resonance imaging (fmri): detecting and classifying distributed patterns of fmri activity in human visual cortex. NeuroImage 2003, 19(2):261-270.
16. Mitchell T., Hutchinson R., Niculescu R., Pereira F., Wang X., Just M., et al. Learning to decode cognitive states from brain images. Mach Learn 2004, 57(1-2):145-175.
17. Ku S.-P., Gretton A., Macke J., Logothetis N.K. Comparison of pattern recognition methods in classifying high-resolution BOLD signals obtained at high magnetic field in monkeys. Magn Reson Imaging 2008, 26(7):1007-1014.
18. Yamashita O., Sato M.-A., Yoshioka T., Tong F., Kamitani Y. Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns. NeuroImage 2008, 42:1414-1429.
19. Burges C.J.C. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 1998, 2(2):121-167.
20. Duda R.O., Hart P.E., Stork D.G. Pattern Classification 2001, John Wiley & Sons, New York. 2nd ed.
21. Kuncheva L.I. Combining Pattern Classifiers. Methods and Algorithms 2004, John Wiley and Sons, N.Y.
22. Brown G. Ensemble learning. Encyclopedia of Machine Learning 2009, Springer, http://www.springer.com/computer/artificial/book/978-0-387-30768-8. C. Sammut, G. Webb (Eds.).
23. Ng A., Jordan M. On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes. Advances in Neural Information Processing Systems (NIPS) 2001, 841-848.
24. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning 2001, Springer, New York.
25. Mitchell T. Machine learning, McGraw Hill, 1997, draft chapter (2005): generative and discriminative classifiers: naive Bayes and logistic regression http://www.cs.cmu.edu/~tom/mlbook/NBayesLogReg.pdf.
26. LaConte S., Strother S., Cherkassky V., Anderson J., Hu X. Support vector machines for temporal classification of block design fmri data. NeuroImage 2005, 26(2):317-329.
27. Mourao-Miranda J., Bokde A.L., Born C., Hampel H., Stetter M. Classifying brain states and determining the discriminating activation patterns: support vector machine on functional mri data. NeuroImage 2005, 28(4):980-995.
28. Mourao-Miranda J., Reynaud E., McGlone F., Calvert G., Brammer M. The impact of temporal compression and space selection on SVM analysis of single-subject and multi- subject fMRI data. NeuroImage 2006, 33(4):1055-1065.
29. Quinlan J.R. C4.5: Programs for Machine Learning 1993, Morgan Kaufmann, San Mateo, Calif.
30. Hand D.J., Yu K. Idiot's Bayes - not so stupid after all?. Int Stat Rev 2001, 69:385-398.
31. Bishop C. Neural Networks for Pattern Recognition 1995, Clarendon Press, Oxford.
32. Allwein E.L., Schapire R.E., Singer Y. Reducing multiclass to binary: a unifying approach for margin classifiers. J Mach Learn Res 2000, 1:113-141.
33. Tumer K., Ghosh J. Error correlation and error reduction in ensemble classifiers. Connect Sci 1996, 8(3/4):385-404.
34. Kuncheva L.I., Whitaker C.J. Measures of diversity in classifier ensembles. Mach Learn 2003, 51:181-207.
35. Kuncheva L., Whitaker C., Shipp C., Duin R. Limits on the majority vote accuracy in classifier fusion. Pattern Anal Appl 2003, 6:22-31.
36. Friedman N., Geiger D., Goldszmid M. Bayesian network classifiers. Mach Learn 1997, 29(2):131-163.
37. Breiman L. Bagging predictors. Mach Learn 1996, 26(2):123-140.
38. Domingos P., Pazzani M. On the optimality of the simple Bayesian classifier under zero-one loss. Mach Learn 1997, 29:103-130.
39. Bauer E., Kohavi R. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Mach Learn 1999, 36:105-142.
40. Valentini G., Dietterich T.G. Bias-variance analysis of support vector machines for the development of SVM-based ensemble methods. J Mach Learn Res 2004, 725-775.
41. Valentini G. Random aggregated and bagged ensembles of SVMs: an empirical bias-variance analysis. Lecture Notes in Computer Science 2004, Vol. 3077:263-272. F. Roli, J. Kittler, T. Windeatt (Eds.).
42. Freund Y., Schapire R.E. Experiments with a new boosting algorithm. Thirteenth International Conference on Machine Learning 1996, 148-156. Morgan Kaufmann, San Francisco.
43. Ho T.K. The random space method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 1998, 20(8):832-844.
44. Lai C., Reinders M.J., Wessels L. Random subspace method for multivariate feature selection. Pattern Recogn Lett 2006, 27(10):1067-1076.
45. Breiman L. Random forests. Mach Learn 2001, 45:5-32.
46. Rodríguez J.J., Kuncheva L.I., Alonso C.J. Rotation forest: a new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 2006, 28(10):1619-1630.
47. Kuncheva L., Rodríguez J.J. Classifier ensembles with a random linear oracle. IEEE Trans Knowl Data Eng 2007, 19(4):500-508.
48. Rodriguez J.J., Kuncheva L.I. Naïve Bayes ensembles with a random oracle. Proc 7th International Workshop on Multiple Classifier Systems, MCS'07, Vol. LNCS 4472, Prague, Czech Republic 2007, 450-458.
49. Golland P., Fischl B. Permutation tests for classification: towards statistical significance in image-based studies. Proceedings of the International Conference on Information Processing in Medical Imaging (IPMI) 2003, 330-341.
50. Nadeau C., Bengio Y. Inference for the generalization error. Mach Learn 2003, 62:239-281.
51. Witten I.H., Frank E. Data Mining: Practical Machine Learning Tools and Techniques 2005, Morgan Kaufmann, San Francisco (CA). 2nd ed.
52. Palatucci M., Carlson A. On the chance accuracies of large collections of classifiers. Proceedings of the 25th International Conference on Machine Learning 2008, 744-751.
53. Guyon I., Weston J., Barnhill S., Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn 2002, 46:389-422.
54. Stearns S. On selecting features for pattern classifiers. 3-d International Conference on Pattern Recognition, Coronado, CA 1976, 71-75.
55. Hall MA. Correlation-based feature subset selection for machine learning, Ph.D. thesis, University of Waikato, Hamilton, New Zealand (1998).
56. Fan R.-E., Chang K.-W., Hsieh C.-J., Wang X.-R., Lin C.-J. LIBLINEAR - a library for large linear classification http://www.csie.ntu.edu.tw/~cjlin/liblinear/.
57. Breiman L. Arcing classifiers. Ann Stat 1998, 26(3):801-849.
58. Pudil P., NovoviČová J., Kittler J. Floating search methods in feature selection. Pattern Recogn Lett 1994, 15:1119-1125.