Applied multivariate statistical analysis
Applied multivariate statistical analysis
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Ranked linear models and sequential patterns recognition
Pattern Analysis & Applications
Selection of high risk patients with ranked models based on the CPL criterion functions
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
| Hi-index | 0.00 |
Designing linear prognostic models on the base of multivariate learning set with censored dependent variable is considered in the paper. The task of linear regression model designing has been reformulated here as a problem of testing the linear separability of two sets. The convex and piecewise linear (CPL) criterion functions are used here both for estimation of the model parameters and for the feature selection task. The feature selection is aimed on neglecting a possibly large amount of independent variables while improving resulting model quality. Particular attention is paid to modeling censored data used in survival analysis. Experiments with the use of the RLS method of gene subset selection in prognostic model selection with the censored dependent variable is also described in the paper.