Abstract
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.
Paper Type
Short Paper
DOI
10.62036/ISD.2026.55
Data-Driven Criteria Reduction in TOPSIS Using LASSO Regression: Application to Offshore Wind Farm Site Selection
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.
Recommended Citation
Kizielewicz, B., Bączkiewicz, A., Wątróbski, J. & Sałabun, W.(2026). Data-Driven Criteria Reduction in TOPSIS Using LASSO Regression: Application to Offshore Wind Farm Site Selection. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.55