Sparse multi-class prediction based on the Group Lasso in multinomial logistic regression

Sparse multi-class prediction based on the Group Lasso in multinomial logistic regression

Sanders, M.A.

Goeman, J. (mentor)
Reinders, M.J.T. (mentor)

Electrical Engineering, Mathematics and Computer Science

Mediamatics

Bioinformatics

2009-11-03

Continuous variable selection using shrinkage procedures have recently been considered as favorable models in a wide range of scientific research; in particular biomedical research. In some cases, it is desirable to select as few predictors as possible, to increase the interpretability of the attained prediction rule. One frequently used shrinkage procedure; the Lasso, imposes a L1 regularization on the regression coefficients of general linear models, inherently leading to sparse prediction rules. When dealing with multi-class prediction in generalized linear models each predictor has a regression coefficient for each class. A major disadvantage is that the Lasso selects individual regression coefficients instead of the more logical selection of predictors. In this paper, we demonstrate a new regularization procedure, based on the Group Lasso in multinomial logistic regression. This results in a lower number of retained predictors, but with similar prediction accuracy when compared to the regular Lasso regularization. To illustrate the new regularization applicability we have employed it on a large cohort of acute myeloid leukemia patients (AML, n=531) who are characterized on a gene expression microarray.

regression
lasso
group lasso
leukemia
multi-class

http://resolver.tudelft.nl/uuid:09903120-2d84-45af-aa61-4ec3c1d1c5a3

2009-11-10

Student theses

master thesis

(c) 2009 Sanders, M.A.