A Poly-Adaptive Metric (PAM) model for multi-objective variable selection in predictive modeling

Music City Center 

High-dimensional datasets require effective variable selection to reduce search space for downstream predictive analyses. Traditionally, statistical significance has been the primary criterion for variable selection. However, statistical significance does not necessarily reflect a variable's predictive utility. Moreover, multiple predictive metrics evaluate different aspects of predictability, yet no existing framework systematically integrates variable selection across these diverse criteria. Here, we propose the Poly-Adaptive Metric (PAM) model, a multi-objective ensemble approach that combines statistical significance with predictive performance metrics to optimize variable selection. The PAM model quantifies the reliability of each selection criterion to construct a unified variable importance matrix that guides the ensembling of different selection strategies. We applied the PAM model to the UK Biobank to construct polygenic risk scores (PRS) and compared its predictive performance against PRS generated using conventional p-value-based SNP selection. Our results demonstrate that the PAM model consistently outperforms p-value-based selection across multiple evaluation metrics.

variable selection

ensemble learning

multi-objective

predictive modeling

polygenic risk score 

Section on Statistics in Genomics and Genetics