2024/11/14 by Mahdi Nouraie, Nouraie, Mahdi, Samuel Müller +1 · 2 citations
Computer Science · Engineering · Mathematics · #Artificial Immune Systems Applications #Computation (stat.CO) #FOS: Computer and information sciences #Fuzzy Systems and Optimization #Machine Learning (stat.ML) #Metaheuristic Optimization Algorithms Research #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2411.09097
openalex publication_date 2024/11/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Stability selection is a widely adopted resampling-based framework for high-dimensional variable selection. This paper seeks to broaden the use of an established stability estimator to evaluate the overall stability of the stability selection results, moving beyond single-variable analysis. We suggest that the stability estimator offers two advantages: it can serve as a reference to reflect the robustness of the results obtained, and it can help identify a Pareto optimal regularization value to improve stability. By determining the regularization value, we calibrate key stability selection parameters, namely, the decision-making threshold and the expected number of falsely selected variables, within established theoretical bounds. In addition, the convergence of stability values over successive sub-samples sheds light on the required number of sub-samples addressing a notable gap in prior studies. The stabplot R package is developed to facilitate the use of the methodology featured in this paper.