The analysis of high-dimensional data, ubiquitous in fields such as genomics, is frequently complicated by the presence of cellwise contamination, where individual cells rather than entire rows are corrupted. This contamination poses a significant challenge to standard variable selection techniques. While recent ensemble methods have introduced deterministic frameworks that partition the predictor space to manage high collinearity, these modern architectures were not designed to handle cellwise contamination, leaving a critical methodological gap. To bridge this gap, we propose the Fast and Scalable Cellwise-Robust Ensemble (FSCRE) algorithm, a novel, multi-stage framework integrating three key statistical stages. First, the algorithm establishes a robust foundation by deriving a cleaned data matrix and a reliable, cellwise-robust covariance structure. Variable selection then proceeds via a competitive ensemble: a robust, correlation-based formulation of the Least-Angle Regression (LARS) algorithm proposes candidates for multiple sub-models, and a cross-validation criterion arbitrates their final assignment. Despite its architectural complexity, the proposed method possesses fundamental theoretical properties, including invariance to data scaling and equivariance to predictor permutation, which establish its objectivity. Through extensive simulations and a bioinformatics application, we demonstrate FSCRE's superior performance in variable selection precision, recall, and predictive accuracy across various contamination scenarios. This work provides a unified framework connecting cellwise-robust estimation with high-performance ensemble learning, with an implementation available on CRAN.

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