Abstract
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.
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Bobrowski, L., Łukaszuk, T. (2012). Prognostic Modeling with High Dimensional and Censored Data. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2012. Lecture Notes in Computer Science(), vol 7377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31488-9_15
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DOI: https://doi.org/10.1007/978-3-642-31488-9_15
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