Abstract
In the theory of semi-supervised learning, we have a training set and a unlabeled data that are employed to fit a prediction model or learner with the help of an iterative algorithm such as the expectation-maximization (EM) algorithm. In this paper a novel non-parametric approach of the so called case-based statistical learning in a low-dimensional classification problem is proposed. This supervised model selection scheme analyzes the discrete set of outcomes in the classification problem by hypothesis-testing and makes assumptions on these outcome values to obtain the most likely prediction model at the training stage. A novel prediction model is described in terms of the output scores of a confidence-based support vector machine classifier under class-hypothesis testing. The estimation of the error rates from a well-trained SVM allows us to propose a non-parametric approach avoiding the use of Gaussian density function-based models in the likelihood ratio test.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Álvarez, I., Górriz, J.M., Ramírez, J., Salas, D., López, M., Puntonet, C.G., Segovia, F.: Alzheimer’s diagnosis using eigenbrains and support vector machines. IET Electron. Lett. 45(1), 165–167 (2009)
Cao, L.J., Tay, F.E.: Support vector machine with adaptive parameters in financial time series forecasting. Trans. Neural Netw. 14(6), 1506–1518 (2003). http://dx.doi.org/10.1109/TNN.2003.820556
Chow, C.: On optimum recognition error and reject tradeoff. IEEE Trans. Inf. Theory 16(1), 41–46 (1970)
Górriz, J.M., Lassl, A., Ramírez, J., Salas-Gonzalez, D., Puntonet, C., Lang, E.: Automatic selection of ROIs in functional imaging using Gaussian mixture models. Neurosci. Lett. 460(2), 108–111 (2009)
Gorriz, J.M., Ramirez, J., Illan, I.A., Martinez-Murcia, F.J., Segovia, F., Salas-Gonzalez, D.: Case-based statistical learning applied to SPECT image classification. In: SPIE Medical Imaging Computer-Aided Diagnosis, vol. 78, pp. 1–4, February 2017
Gorriz, J.M., Ramirez, J., Lang, E.W., Puntonet, C.G.: Jointly Gaussian PDF-based likelihood ratio test for voice activity detection. IEEE Trans. Audio Speech Lang. Process. 16(8), 1565–1578 (2008)
Górriz, J.M., Segovia, F., Ramírez, J., Lassl, A., Salas-Gonzalez, D.: Gmm based SPECT image classification for the diagnosis of Alzheimer’s disease. Appl. Soft Comput. 11(2), 2313–2325 (2011). http://dx.doi.org/10.1016/j.asoc.2010.08.012
Gorriz, J., Ramirez, J., Lassl, A., Salas-Gonzalez, D., Lang, E., Puntonet, C., Alvarez, I., Lopez, M., Gomez-Rio, M.: Automatic computer aided diagnosis tool using component-based SVM. In: IEEE Nuclear Science Symposium Conference Record, NSS 2008, pp. 4392–4395. IEEE (2008)
Guyon, I.M., Gunn, S.R., Nikravesh, M., Zadeh, L. (eds.): Feature Extraction, Foundations and Applications. Springer, Heidelberg (2006)
Illán, I., Górriz, J.M., Ramírez, J., Salas-González, D., López, M., Segovia, F., Chaves, R., Gómez-Rio, M., Puntonet, C.: 18F-FDG PET imaging analysis for computer aided Alzheimer’s diagnosis. Inf. Sci. 181(4), 903–916 (2011)
James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning with Applications in R. Springer, Heidelberg (2013)
Kay, S.M.: Fundamentals of Statistical Signal Processing: Detection Theory. Prentice Hall Signal Processing Series, vol. II. Prentice Hall, Upper Saddle River (1993)
Khedher, L., Ramirez, J., Gorriz, J.M., Brahim, A., Segovia, F., Alzheimer’s Disease Neuroimaging Initiative, et al.: Early diagnosis of Alzheimer’s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images. Neurocomputing 151, 139–150 (2015)
Li, M., Sethi, I.K.: Confidence-based classifier design. Pattern Recogn. 39(7), 1230–1240 (2006)
Ortiz, A., Gorriz, J.M., Ramirez, J., Martinez-Murcia, F.J., Initiative, A.D.N., et al.: LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer’s disease. Pattern Recogn. Lett. 34(14), 1725–1733 (2013)
Platt, J.C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in Large Margin Classifiers, pp. 61–74. MIT Press, Cambridge (1999)
Segovia, F., Gorriz, J., Ramirez, J., Alvarez, I., Jimenez-Hoyuela, J., Ortega, S.: Improved Parkinsonism diagnosis using a partial least squares based approach. Med. Phys. 39(7), 4395–4403 (2012)
Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)
Weiner, M.W., Górriz, J.M., Ramírez, J., Castiglioni, I.: Statistical signal processing in the analysis, characterization and detection of Alzheimer’s disease. Curr. Alzheimer Res. 13(5), 466–468 (2016)
Wernick, M.N., Yang, Y., Brankov, J.G., Yourganov, G., Strother, S.C.: Machine learning in medical imaging. IEEE Sig. Process. Mag. 27(4), 25–38 (2010)
Acknowledgement
This work was partly supported by the MINECO under the TEC2015-64718-R project and the Consejería de Economía, Inno- vación, Ciencia y Empleo (Junta de Andalucía, Spain) under the Excellence Project P11-TIC-7103.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Górriz, J.M. et al. (2017). Case-Based Statistical Learning: A Non Parametric Implementation Applied to SPECT Images. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science(), vol 10337. Springer, Cham. https://doi.org/10.1007/978-3-319-59740-9_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-59740-9_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59739-3
Online ISBN: 978-3-319-59740-9
eBook Packages: Computer ScienceComputer Science (R0)