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

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

Computational Statistics & Data Analysis, 2012, vol. 56, issue 3, 611-628

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.

Keywords: Classification; Longitudinal data; Least squares; Support vector machine; Kernel method; Mixed model (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:3:p:611-628

DOI: 10.1016/j.csda.2011.09.008

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