A mixed effects least squares support vector machine model for classification of longitudinal data
| Autor: | Geert Verbeke, Jan Luts, Sabine Van Huffel, Geert Molenberghs, Johan A. K. Suykens |
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| Rok vydání: | 2012 |
| Předmět: |
Statistics and Probability
Structured support vector machine business.industry Applied Mathematics Explained sum of squares Pattern recognition Generalized least squares Generalized linear mixed model Support vector machine Relevance vector machine Computational Mathematics ComputingMethodologies_PATTERNRECOGNITION Computational Theory and Mathematics Least squares support vector machine Artificial intelligence Total least squares business Mathematics |
| Zdroj: | Computational Statistics & Data Analysis. 56:611-628 |
| ISSN: | 0167-9473 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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