Min-Marginal Power Indices for Multi-Objective Feature Selection

The paper introduces the Min-Marginal Power Index (MinPI), a game-theoretic feature selection method that labels features as harmful, neutral, or decisive based on whether adding them to a coalition drops utility below a threshold. MinPI enables conservative feature removal without a score cutoff, and its application to six benchmark datasets removed 12.5% of features with negligible loss in held‑out utility. The method also improves multi‑objective search performance by prefiltering features, especially in datasets with many removable features.

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