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Case-Based Statistical Learning: A Non Parametric Implementation Applied to SPECT Images

  • J. M. GórrizEmail author
  • J. Ramírez
  • F. J. Martinez-Murcia
  • I. A. Illán
  • F. Segovia
  • D. Salas-González
  • A. Ortiz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10337)

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.

Keywords

Statistical learning and decision theory Support vector machines (SVM) Hypothesis testing Partial least squares Conditional-error rate 

Notes

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.

References

  1. 1.
    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)Google Scholar
  2. 2.
    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 CrossRefGoogle Scholar
  3. 3.
    Chow, C.: On optimum recognition error and reject tradeoff. IEEE Trans. Inf. Theory 16(1), 41–46 (1970)CrossRefzbMATHGoogle Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    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 2017Google Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    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 CrossRefGoogle Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    Guyon, I.M., Gunn, S.R., Nikravesh, M., Zadeh, L. (eds.): Feature Extraction, Foundations and Applications. Springer, Heidelberg (2006)Google Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning with Applications in R. Springer, Heidelberg (2013)CrossRefzbMATHGoogle Scholar
  12. 12.
    Kay, S.M.: Fundamentals of Statistical Signal Processing: Detection Theory. Prentice Hall Signal Processing Series, vol. II. Prentice Hall, Upper Saddle River (1993)zbMATHGoogle Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    Li, M., Sethi, I.K.: Confidence-based classifier design. Pattern Recogn. 39(7), 1230–1240 (2006)CrossRefzbMATHGoogle Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    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)Google Scholar
  17. 17.
    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)CrossRefGoogle Scholar
  18. 18.
    Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar
  19. 19.
    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)CrossRefGoogle Scholar
  20. 20.
    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)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • J. M. Górriz
    • 1
    Email author
  • J. Ramírez
    • 1
  • F. J. Martinez-Murcia
    • 1
  • I. A. Illán
    • 3
  • F. Segovia
    • 1
  • D. Salas-González
    • 1
  • A. Ortiz
    • 2
  1. 1.Department of Signal Theory and CommunicationsUniversity of GranadaGranadaSpain
  2. 2.Department of Communications EngineeringUniversidad de MalagaMálagaSpain
  3. 3.Department of Scientific ComputingThe Florida State UniversityTallahasseeUSA

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