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|Title: ||A mixed effects least squares support vector machine model for classification of longitudinal data|
|Authors: ||Luts, Jan|
Van Huffel, Sabine
Suykens, Johan A. K.
|Issue Date: ||2012|
|Publisher: ||ELSEVIER SCIENCE BV|
|Citation: ||COMPUTATIONAL STATISTICS & DATA ANALYSIS, 56 (3), p. 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. (C) 2011 Elsevier B.V. All rights reserved.|
|Notes: ||[Luts, Jan; Van Huffel, Sabine; Suykens, Johan A. K.] Katholieke Univ Leuven, Dept Elect Engn ESAT, Res Div SCD, B-3001 Louvain, Belgium. [Luts, Jan; Van Huffel, Sabine; Suykens, Johan A. K.] IBBT KU Leuven Future Hlth Dept, Louvain, Belgium. [Molenberghs, Geert] Univ Hasselt, I BioStat, B-3590 Diepenbeek, Belgium. [Molenberghs, Geert; Verbeke, Geert] Katholieke Univ Leuven, I BioStat, B-3000 Louvain, Belgium.
|Link to publication: ||ftp://ftp.esat.kuleuven.ac.be/sista/jluts/reports/mixedEffectsLSSVM.pdf|
|ISI #: ||000298122600014|
|Type: ||Journal Contribution|
|Validation: ||ecoom, 2013|
|Appears in Collections: ||Research publications|
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|Published version||1.8 MB||Adobe PDF|
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