MinShap: A Shapley-Based Framework for Feature Redundancy
Shapley values provide a flexible framework for attributing feature contributions to model predictions, but they are not naturally suited for feature selection: a feature may receive a positive attribution even when it is redundant given the remaining variables. In this paper, we introduce \textbf{MinShap}, a general framework for identifying \emph{important} or \emph{non-redundant} features through conditional importance functionals $VI_j^S$. Rather than averaging feature contributions across conditioning sets, MinShap aggregates them using the \emph{minimum}, thereby testing whether a feature remains relevant under every conditioning context. We show that, under a simple \emph{null mono...
| Authors | Chenghui Zheng, Garvesh Raskutti |
|---|---|
| Primary category | stat.ML |
| Published | 16 April 2026 |
| Last updated at source | 19 July 2026 |
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