Ensembling variable selectors by stability selection for the Cox model

Abstract

As a pivotal tool to build interpretive models, variable selection plays an increasingly important role in high-dimensional data analysis. In recent years, variable selection ensembles (VSEs) have gained much interest due to their many advantages. Stability selection (Meinshausen and Bühlmann, 2010), a VSE technique based on subsampling in combination with a base algorithm like lasso, is an effective method to control false discovery rate (FDR) and to improve selection accuracy in linear regression models. By adopting lasso as a base learner, we attempt to extend stability selection to handle variable selection problems in a Cox model. According to our experience, it is crucial to set the regularization region Λ in lasso and the parameter min properly so that stability selection can work well. To the best of our knowledge, however, there is no literature addressing this problem in an explicit way. Therefore, we first provide a detailed procedure to specify Λ and min . Then, some simulated and real-world data with various censoring rates are used to examine how well stability selection performs. It is also compared with several other variable selection approaches. Experimental results demonstrate that it achieves better or competitive performance in comparison with several other popular techniques.

A novel heterogeneous ensemble approach to variable selection for gas-liquid two-phase CO2 flow metering

“…In general, the ensemble approach for variable selection can be classified into two categories: homogeneous and heterogeneous approaches. (Zhu et al, 2011;Zhou., 2012;Li et al, 2017). The homogeneous ensemable approach is to use the same selection method on different datasets while the heterogeneous ensemble approach is to train different selection algorithms on the same dataset.…”