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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/13013

Title: A mixed effects least squares support vector machine model for classification of longitudinal data
Authors: Luts, Jan
Molenberghs, Geert
Verbeke, Geert
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. jan.luts@esat.kuleuven.be
URI: http://hdl.handle.net/1942/13013
Link to publication: ftp://ftp.esat.kuleuven.ac.be/sista/jluts/reports/mixedEffectsLSSVM.pdf
DOI: 10.1016/j.csda.2011.09.008
ISI #: 000298122600014
ISSN: 0167-9473
Category: A1
Type: Journal Contribution
Validation: ecoom, 2013
Appears in Collections: Research publications

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