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A mixed effects least squares support vector machine model for classification of longitudinal data

Author

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  • Luts, Jan
  • Molenberghs, Geert
  • Verbeke, Geert
  • Van Huffel, Sabine
  • Suykens, Johan A.K.

Abstract

A mixed effects least squares support vector machine (LS-SVM) classifier is introduced to extend the standard LS-SVM classifier for handling longitudinal data. The mixed effects LS-SVM model contains a random intercept and allows to classify highly unbalanced data, in the sense that there is an unequal number of observations for each case at non-fixed time points. The methodology consists of a regression modeling and a classification step based on the obtained regression estimates. Regression and classification of new cases are performed in a straightforward manner by solving a linear system. It is demonstrated that the methodology can be generalized to deal with multi-class problems and can be extended to incorporate multiple random effects. The technique is illustrated on simulated data sets and real-life problems concerning human growth.

Suggested Citation

  • Luts, Jan & Molenberghs, Geert & Verbeke, Geert & Van Huffel, Sabine & Suykens, Johan A.K., 2012. "A mixed effects least squares support vector machine model for classification of longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 611-628.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:3:p:611-628
    DOI: 10.1016/j.csda.2011.09.008
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    References listed on IDEAS

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    Cited by:

    1. Ana Arribas-Gil & Rolando De la Cruz & Emilie Lebarbier & Cristian Meza, 2015. "Classification of longitudinal data through a semiparametric mixed-effects model based on lasso-type estimators," Biometrics, The International Biometric Society, vol. 71(2), pages 333-343, June.
    2. Luts, Jan & Ormerod, John T., 2014. "Mean field variational Bayesian inference for support vector machine classification," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 163-176.
    3. Zhang, Xin & Jeske, Daniel R. & Li, Jun & Wong, Vance, 2016. "A sequential logistic regression classifier based on mixed effects with applications to longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 238-249.
    4. Huang, Xiaolin & Shi, Lei & Suykens, Johan A.K., 2014. "Asymmetric least squares support vector machine classifiers," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 395-405.
    5. Joanna F Dipnall & Richard Page & Lan Du & Matthew Costa & Ronan A Lyons & Peter Cameron & Richard de Steiger & Raphael Hau & Andrew Bucknill & Andrew Oppy & Elton Edwards & Dinesh Varma & Myong Chol , 2021. "Predicting fracture outcomes from clinical registry data using artificial intelligence supplemented models for evidence-informed treatment (PRAISE) study protocol," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-12, September.

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