PGDS is a new explainable AI method for many-objective optimization that automates target selection via partitioning and identifies influential decision variables through distance-based sensitivity analysis.
Journal of Machine Learning Research1, 1–33 (2025), arXiv:2505.15516v1 [cs.LG]
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Partition-Guided Distance Saliency: Bridging Decision and Objective Spaces in Many-Objective Optimization
PGDS is a new explainable AI method for many-objective optimization that automates target selection via partitioning and identifies influential decision variables through distance-based sensitivity analysis.