Enhancing SVM for survival data using local invariances and weighting.

ABSTRACT:

Background

The necessity to analyze medium-throughput data in epidemiological studies with small sample size, particularly when studying biomedical data may hinder the use of classical statistical methods. Support vector machines (SVM) models can be successfully applied in this setting because they are a powerful tool to analyze data with large number of predictors and limited sample size, especially when handling binary outcomes. However, biomedical research often involves analysis of time-to-event outcomes and has to account for censoring. Methods to handle censored data in the SVM framework can be divided into two classes: those based on support vector regression (SVR) and those based on binary classification. Methods based on SVR seem to be suboptimal to handle sparse data and

SUBMITTER: Sanz H 

PROVIDER: S-EPMC7236493 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature