, Volume 16, Issue 2, pp 253–268 | Cite as

Fast, Accurate, and Stable Feature Selection Using Neural Networks

  • James Deraeve
  • William H. Alexander
Original Article


Multi-voxel pattern analysis often necessitates feature selection due to the high dimensional nature of neuroimaging data. In this context, feature selection techniques serve the dual purpose of potentially increasing classification accuracy and revealing sets of features that best discriminate between classes. However, feature selection techniques in current, widespread use in the literature suffer from a number of deficits, including the need for extended computational time, lack of consistency in selecting features relevant to classification, and only marginal increases in classifier accuracy. In this paper we present a novel method for feature selection based on a single-layer neural network which incorporates cross-validation during feature selection and stability selection through iterative subsampling. Comparing our approach to popular alternative feature selection methods, we find increased classifier accuracy, reduced computational cost and greater consistency with which relevant features are selected. Furthermore, we demonstrate that importance mapping, a technique used to identify voxels relevant to classification, can lead to the selection of irrelevant voxels due to shared activation patterns across categories. Our method, owing to its relatively simple architecture, flexibility and speed, can provide a viable alternative for researchers to identify sets of features that best discriminate classes.


Feature selection fMRI MVPA Machine learning 



This research was supported by FWO-Flanders Odysseus II Award #G.OC44.13 N to WHA.

Compliance with Ethical Standards

Conflict of Interest

We report no conflicts of interest.


  1. Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Muller, A., Kossaifi, J., … Varoquaux, G. (2014). Machine learning for neuroimaging with Scikit-learn. arXiv:1412.3919 [Cs, Stat]. Retrieved from
  2. Bolón-Canedo, V., Sánchez-Maroño, N., & Alonso-Betanzos, A. (2013). A review of feature selection methods on synthetic data. Knowledge and Information Systems, 34(3), 483–519. Scholar
  3. Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on computational learning theory (pp. 144–152). New York: ACM. Scholar
  4. Cao, L. J., & Chong, W. K. (2002). Feature extraction in support vector machine: a comparison of PCA, XPCA and ICA. In Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP ‘02 (Vol. 2, pp. 1001–1005 vol. 2).
  5. Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16–28. Scholar
  6. Chou, C. A., Kampa, K., Mehta, S. H., Tungaraza, R. F., Chaovalitwongse, W. A., & Grabowski, T. J. (2014). Voxel selection framework in multi-voxel pattern analysis of fMRI data for prediction of neural response to visual stimuli. IEEE Transactions on Medical Imaging, 33(4), 925–934. Scholar
  7. Chu, C., Hsu, A.-L., Chou, K.-H., Bandettini, P., & Lin, C. (2012). Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images. NeuroImage, 60(1), 59–70. Scholar
  8. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. Scholar
  9. 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(2), 261–270. Scholar
  10. Das, S. (2001). Filters, wrappers and a boosting-based hybrid for feature selection. In Proceedings of the eighteenth international conference on machine learning (pp. 74–81). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc. Retrieved from Scholar
  11. De Martino, F., Valente, G., Staeren, N., Ashburner, J., Goebel, R., & Formisano, E. (2008). Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. NeuroImage, 43(1), 44–58. Scholar
  12. Dernoncourt, D., Hanczar, B., & Zucker, J.-D. (2014). Analysis of feature selection stability on high dimension and small sample data. Computational Statistics & Data Analysis, 71, 681–693. Scholar
  13. Ding, C., & Peng, H. (2005). Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology, 3(2), 185–205. Scholar
  14. Dittman, D., Khoshgoftaar, T. M., Wald, R., & Wang, H. (2011). Stability Analysis of Feature Ranking Techniques on Biological Datasets. In 2011 I.E. International Conference on Bioinformatics and Biomedicine (pp. 252–256).
  15. Do, L.-N., Yang, H.-J., Kim, S.-H., Lee, G.-S., & Kim, S.-H. (2015). A multi-voxel-activity-based feature selection method for human cognitive states classification by functional magnetic resonance imaging data. Cluster Computing, 18(1), 199–208. Scholar
  16. Fan, M., & Chou, C.-A. (2016). Exploring stability-based voxel selection methods in MVPA using cognitive neuroimaging data: A comprehensive study. Brain Informatics, 3(3), 193–203. Scholar
  17. Fleuret, F. (2004). Fast binary feature selection with conditional mutual information. Journal of Machine Learning Research, 5(Nov), 1531–1555.Google Scholar
  18. Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3(Mar), 1157–1182.Google Scholar
  19. Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for Cancer classification using support vector machines. Machine Learning, 46(1–3), 389–422. Scholar
  20. Hall, M. A. (1998). Correlation-based feature selection for machine learning.Google Scholar
  21. Haury, A.-C., Gestraud, P., & Vert, J.-P. (2011). The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures. PLoS One, 6(12), e28210. Scholar
  22. Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293(5539), 2425–2430. Scholar
  23. Hebart, M. N., Görgen, K., & Haynes, J.-D. (2015). The decoding toolbox (TDT): A versatile software package for multivariate analyses of functional imaging data. Frontiers in Neuroinformatics, 8.
  24. Johnson, J. D., McDuff, S. G. R., Rugg, M. D., & Norman, K. A. (2009). Recollection, familiarity, and cortical reinstatement: A multi-voxel pattern analysis. Neuron, 63(5), 697–708. Scholar
  25. Kalousis, A., Prados, J., & Hilario, M. (2005). Stability of feature selection algorithms. In Fifth IEEE International Conference on Data Mining (ICDM’05) (p. 8 pp.-).
  26. Kalousis, A., Prados, J., & Hilario, M. (2007). Stability of feature selection algorithms: A study on high-dimensional spaces. Knowledge and Information Systems, 12(1), 95–116. Scholar
  27. Kerr, W. T., Douglas, P. K., Anderson, A., & Cohen, M. S. (2014). The utility of data-driven feature selection: Re: Chu et al. 2012. NeuroImage, 84, 1107–1110. Scholar
  28. Kirk, P., Witkover, A., Bangham, C. R. M., Richardson, S., Lewin, A. M., & Stumpf, M. P. H. (2013). Balancing the robustness and predictive performance of biomarkers. Journal of Comparative Biology, 20(12), 979–989. Scholar
  29. Kononenko, I., & Simec, E. (1995). Induction of decision trees using Relieff. In Proceedings of the ISSEK94 workshop on mathematical and statistical methods in artificial intelligence (pp. 199–220). Springer, Vienna.
  30. Kononenko, I., Šimec, E., & Robnik-Šikonja, M. (1997). Overcoming the myopia of inductive learning algorithms with RELIEFF. Applied Intelligence, 7(1), 39–55. Scholar
  31. Křížek, P., Kittler, J., & Hlaváč, V. (2007). Improving stability of feature selection methods. In Computer Analysis of Images and Patterns (pp. 929–936). Springer, Berlin, Heidelberg., Improving Stability of Feature Selection Methods.
  32. Kuncheva, L. I., Rodriguez, J. J., Plumpton, C. O., Linden, D. E. J., & Johnston, S. J. (2010). Random subspace ensembles for fMRI classification. IEEE Transactions on Medical Imaging, 29(2), 531–542. Scholar
  33. Lewis-Peacock, J. A., Drysdale, A. T., Oberauer, K., & Postle, B. R. (2011). Neural evidence for a distinction between short-term memory and the focus of attention. Journal of Cognitive Neuroscience, 24(1), 61–79. Scholar
  34. Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. (2017). Feature Selection: A Data Perspective. ACM Computing. Surveys, 50(6), 94:1–94:45. :
  35. Liu, H., & Setiono, R. (1995). Chi2: feature selection and discretization of numeric attributes. In Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence (pp. 388–391).
  36. Ma, S., & Huang, J. (2008). Penalized feature selection and classification in bioinformatics. Briefings in Bioinformatics, 9(5), 392–403. Scholar
  37. Mahmoudi, A., Takerkart, S., Regragui, F., Boussaoud, D., & Brovelli, A. (2012). Multivoxel pattern analysis for fMRI data: A review. Computational and Mathematical Methods in Medicine, 2012, e961257.
  38. McDuff, S. G. R., Frankel, H. C., & Norman, K. A. (2009). Multivoxel pattern analysis reveals increased memory targeting and reduced use of retrieved details during single-agenda source monitoring. Journal of Neuroscience, 29(2), 508–516. Scholar
  39. Meinshausen, N., & Bühlmann, P. (2010). Stability selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(4), 417–473. Scholar
  40. Michel, V., Damon, C., & Thirion, B. (2008). Mutual information-based feature selection enhances fMRI brain activity classification. In 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (pp. 592–595).
  41. Mwangi, B., Tian, T. S., & Soares, J. C. (2014). A review of feature reduction techniques in neuroimaging. Neuroinformatics, 12(2), 229–244. Scholar
  42. Nie, F., Xiang, S., Jia, Y., Zhang, C., & Yan, S. (2008). Trace ratio criterion for feature selection. In In AAAI (pp. 671–676).Google Scholar
  43. Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006). Beyond mind-reading: Multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10(9), 424–430. Scholar
  44. O’Toole, A. J., Jiang, F., Abdi, H., & Haxby, J. V. (2005). Partially distributed representations of objects and faces in ventral temporal cortex. Journal of Cognitive Neuroscience, 17(4), 580–590. Scholar
  45. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12(Oct), 2825–2830.Google Scholar
  46. Peng, H., Long, F., & Ding, C. (2005). Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226–1238. Scholar
  47. Polyn, S. M., Natu, V. S., Cohen, J. D., & Norman, K. A. (2005). Category-specific cortical activity precedes retrieval during memory search. Science, 310(5756), 1963–1966. Scholar
  48. Ross, B. C. (2014). Mutual information between discrete and continuous data sets., Mutual Information between Discrete and Continuous Data Sets. PloS One, PLoS ONE, 9, 9(2, 2), e87357–e87357.,
  49. Saarimäki, H., Gotsopoulos, A., Jääskeläinen, I. P., Lampinen, J., Vuilleumier, P., Hari, R., Sams, M., & Nummenmaa, L. (2016). Discrete neural signatures of basic emotions. Cerebral Cortex, 26(6), 2563–2573. Scholar
  50. Saeys, Y., Inza, I., & Larrañaga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19), 2507–2517. Scholar
  51. Saeys, Y., Abeel, T., & Peer, Y. V. de. (2008). Robust feature selection using ensemble feature selection techniques. In Machine Learning and Knowledge Discovery in Databases (pp. 313–325). Springer, Berlin, Heidelberg., Robust Feature Selection Using Ensemble Feature Selection Techniques.
  52. Sayres, R., Ress, D., & Grill-Spector, K. (2005). Identifying distributed object representations in human Extrastriate visual cortex. In Proceedings of the 18th international conference on neural information processing systems (pp. 1169–1176). Cambridge: MIT Press Retrieved from Scholar
  53. Stiglic, G., & Kokol, P. (2010). Stability of ranked gene lists in large microarray analysis studies. BioMed Research International, 2010, e616358. Scholar
  54. Tohka, J., Moradi, E., Huttunen, H., & Initiative, A. D. N. (2016). Comparison of feature selection techniques in machine learning for anatomical brain MRI in dementia. Neuroinformatics, 14(3), 279–296. Scholar
  55. Toloşi, L., & Lengauer, T. (2011). Classification with correlated features: Unreliability of feature ranking and solutions. Bioinformatics, 27(14), 1986–1994. Scholar
  56. Turney, P. (1995). Technical note: Bias and the quantification of stability. Machine Learning, 20(1–2), 23–33. Scholar
  57. Vergara, J. R., & Estévez, P. A. (2014). A review of feature selection methods based on mutual information. Neural Computing and Applications, 24(1), 175–186. Scholar
  58. Wang, Y., Li, Z., Wang, Y., Wang, X., Zheng, J., Duan, X., & Chen, H. (2015). A Novel Approach for Stable Selection of Informative Redundant Features from High Dimensional fMRI Data. arXiv:1506.08301 [Cs, Stat]. Retrieved from
  59. Wright, S. (1965). The interpretation of population structure by F-statistics with special regard to Systems of Mating. Evolution, 19(3), 395–420. Scholar
  60. Yan, S., Yang, X., Wu, C., Zheng, Z., & Guo, Y. (2014). Balancing the stability and predictive performance for multivariate voxel selection in fMRI study. In Brain Informatics and Health (pp. 90–99). Springer, Cham., Balancing the Stability and Predictive Performance for Multivariate Voxel Selection in fMRI Study.
  61. Zeithamova, D., de Araujo Sanchez, M.-A., & Adke, A. (2017). Trial timing and pattern-information analyses of fMRI data. NeuroImage, 153(Supplement C), 221–231.
  62. Zhao, Z., & Liu, H. (2007). Spectral feature selection for supervised and unsupervised learning. In Proceedings of the 24th international conference on machine learning (pp. 1151–1157). New York: ACM. Scholar
  63. Zhao, Z., Wang, L., Liu, H., & Ye, J. (2013). On similarity preserving feature selection. IEEE Transactions on Knowledge and Data Engineering, 25(3), 619–632. Scholar

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Authors and Affiliations

  1. 1.Department of Experimental PsychologyGhent UniversityGhentBelgium

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