Papers › LLpowershap: Logistic Loss-based Automated Shapley Values Feature Selection Method
LLpowershap: Logistic Loss-based Automated Shapley Values Feature Selection Method
Iqbal Madakkatel, Elina Hyppönen
Shapley values have been used extensively in machine learning, not only to explain black box machine learning models, but among other tasks, also to conduct model debugging, sensitivity and fairness analyses and to select important features for robust modelling and for further follow-up analyses. Shapley values satisfy certain axioms that promote fairness in distributing contributions of features toward prediction or reducing error, after accounting for non-linear relationships and interactions when complex machine learning models are employed. Recently, a number of feature selection methods utilising Shapley values have been introduced. Here, we present a novel feature selection method, LLpowershap, which makes use of loss-based Shapley values to identify informative features with minimal noise among the selected sets of features. Our simulation results show that LLpowershap not only identifies higher number of informative features but outputs fewer noise features compared to other state-of-the-art feature selection methods. Benchmarking results on four real-world datasets demonstrate higher or at par predictive performance of LLpowershap compared to other Shapley based wrapper methods, or filter methods.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections