Abstract
Recently we proposed a feature selection method based on stability theory. In the present work we present an evaluation of its performance in different contexts through a grid search performed in a subset of its parameters space. The main contributions of this work are: we show that the method can improve the classification accuracy in relation to the wholebrain in different functional datasets; we evaluate the parameters influence in the results, getting some insight in reasonable ranges of values; and we show that combinations of parameters that yield the best accuracies are stable (i.e., they have low rates of false positive selections).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Mourao-Miranda, J., Bokde, A., Born, C., Hampel, H., Stetter, M.: Classifying brain states and determining the discriminating activation patterns: Support vector machine on functional mridata. Neuroimage 95, 980–995 (2005)
Fu, C.H., Mourao-Miranda, J., Costafreda, S.G., Khanna, A., Marquand, A.F., Williams, S.C., Brammer, M.J.: Pattern classification of sad facial processing: toward the development of neurobiological markers in depression. Biol. Psychiatry 63(7), 656–662 (2008)
Koutsouleris, N., Meisenzahl, E., Davatzikos, C., Bottlender, R., Frodl, T., Scheuerecker, J., Schmitt, G., Zetzsche, T., Decker, P., Reiser, M., Möller, H., Gaser, C.: Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. Arch. Gen. Psychiatry 76, 700–712 (2009)
Marquand, A., Howard, M., Brammer, M., Chu, C., Coen, S., Mouro-Miranda, J.: Quantitative pre- diction of subjective pain intensity from whole-brain fmri data using gaussian processes. Neuroimage 49, 2178–2189 (2010)
Ecker, C., Rocha-Rego, V., Johnston, P., Mourao-Miranda, J., Marquand, A., Daly, E.M., Brammer, M.J., Murphy, C., Murphy, D.G., MRC AIMS Consortium: Investigating the predictive value of whole-brain structural mr scans in autism: a pattern classification approach. Neuroimage 49, 44–56 (2010)
Cuingnet, R., Gerardin, E., Tessieras, J., Auzias, G., Lehéricy, S., Habert, M.O., Chupin, M., Benali, H., Colliot, O.: Alzheimer’s Disease Neuroimaging Initiative: Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 56(2), 766–781 (2010)
Guyon, I., Elisseefi, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
De Martino, F., Valente, G., Staeren, N., Ashburner, J., Goebel, R., Formisano, E.: Combining 19 multivariate voxel selection and support vector machines for mapping and classification of fmri spatial patterns. Neuroimage 43, 44–58 (2008)
Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Haussler, D. (ed.) 5th Annual ACM Workshop on COLT, pp. 144–152. ACM Press, Pittsburgh (1992)
Vapnik, V.: Statistical Learning Theory. Wiley (1998)
Langs, G., Menze, B., Lashkari, D., Golland, P.: Detecting stable distributed patterns of brain activation using gini contrast. Neuroimage 56, 497–507 (2011)
Rondina, J.M., Marquand, A.F., Hahn, T., Shawe-Taylor, J., Mourao-Miranda, J.: Selecting features based on stability to classify depressed patients in fMRI. In: 17th Annual Meeting of the Organization for Human Brain Mapping, Quebec City, abstract 4181 (2011)
Meinshausen, N., Buhlmann, P.: Stability selection. Journal of the Royal Statistical Society 72, 417–473 (2010)
Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society 58, 267–288 (1996)
Bunea, F., She, Y., Ombao, H., Gongvatana, A., Devlin, K., Cohen, R.: Penalized least squares regression methods and applications to neuroimaging. Neuroimage 55(4), 1519–1527 (2011)
Wager, T.D., Atlas, L.Y., Leotti, L.A., Rilling, J.K.: Predicting individual differences in placebo analgesia: contributions of brain activity during anticipation and pain experience. Journal of Neuroscience 31(2), 439–452 (2011)
Ng, B., Abugharbieh, R.: Generalized Sparse Regularization with Application to fMRI Brain Decoding. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 612–623. Springer, Heidelberg (2011)
Ng, B., Abugharbieh, R., Varoquaux, G., Poline, J.B., Thirion, B.: Connectivity-Informed fMRI Activation Detection. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 285–292. Springer, Heidelberg (2011)
Funamizu, A., Kanzaki, R., Takahashi, H.: Distributed representation of tone frequency in highly decodable spatio-temporal activity in the auditory cortex. Neural Netw. 24, 321–322 (2011)
Fan, Y., Shen, D., Davatzikos, C.: Classification of Structural Images via High-Dimensional Image Warping, Robust Feature Extraction, and SVM. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 1–8. Springer, Heidelberg (2005)
Mourao-Miranda, J., Reinders, A.A.T.S., Rocha-Rego, V., Lappin, J., Rondina, J., Morgan, C., Morgan, K.D., Fearon, P., Jones, P.B., Doody, G.A., Murray, R.M., Kapur, S., Dazzan, P.: Individualised Prediction of Illness Course at the First Psychotic Episode: a Support Vector Machine MRI Study. Psychological Medicine (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rondina, J.M., Shawe-Taylor, J., Mourão-Miranda, J. (2012). A New Feature Selection Method Based on Stability Theory – Exploring Parameters Space to Evaluate Classification Accuracy in Neuroimaging Data. In: Langs, G., Rish, I., Grosse-Wentrup, M., Murphy, B. (eds) Machine Learning and Interpretation in Neuroimaging. Lecture Notes in Computer Science(), vol 7263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34713-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-34713-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34712-2
Online ISBN: 978-3-642-34713-9
eBook Packages: Computer ScienceComputer Science (R0)