Feature Stability as a Trust Layer for Feature Selection: Resampling-Based Recurrence Profiles Beyond Predictive Performance

Feature selection in high-dimensional studies is conventionally evaluated by the predictive performance of the features it returns, but predictive performance does not indicate whether the same subset would be selected again under a reasonable perturbation of the data. We propose a feature-stability profile, reported as a diagnostic layer beside predictive performance rather than in place of it. The profile is assembled from established quantities: per-feature selection frequency across repeated stratified resamples, a chance-corrected stability summary, recurrent sets reported across a sweep of descriptive cutoffs, a random-selection baseline, and the held-out predictive performance recorded on the same resamples. We examine it in two settings. In a controlled synthetic study, the informative support is planted by construction, so recovery can be measured directly; in an illustrative application across high-dimensional binary datasets, no such support exists, and a broader exploratory roster is reported as supporting results. Under planted support, predictive performance, subset stability, and support recovery can diverge rather than decline together: at an intermediate signal level, performance can remain relatively preserved while exact recovery falls, and some low exact overlap reflects substitution among redundant alternatives. On real data, recurrent features are treated as recurrent candidates, not recovered or validated features; selectors reaching near-equal area under the receiver operating characteristic curve (AUC) can differ about twofold in chance-corrected recurrence. The contribution is diagnostic and integrative, not a new selector, a new metric, a benchmark ranking, or an error-controlled procedure, making the reliability of a selected feature set visible rather than assumed.