Multi-criteria decision analysis often involves large numbers of criteria, which increases cognitive burden and data acquisition costs. This paper proposes a framework that integrates TOPSIS with LASSO regression to identify a compact, interpretable subset of criteria that preserves the ranking produced by the full model. Unlike PCA-based approaches, the method operates in the original criterion space and retains direct interpretability. The framework is demonstrated on seven Polish offshore wind farms evaluated against 33 criteria. LASSO selects 8 criteria at the cross-validated penalty level, yielding Spearman $\rho = 0.89$ and Kendall $\tau = 0.71$ relative to the reference ranking, with no alternative shifting by more than one position. Bootstrap stability selection identifies a robust core of four criteria, and weight perturbation analysis confirms robustness. The trade-off analysis also shows that smaller retrospective subsets can reach higher rank agreement, which exposes the gap between predictive optimization and rank preservation.