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.
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Pith papers citing it
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2026 2verdicts
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An experimental evaluation of learned spatial indexes derives a decision tree for index selection under varying data skew, query selectivity, and storage conditions, validated on real point sets.
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Evaluating Learned Spatial Indexes
An experimental evaluation of learned spatial indexes derives a decision tree for index selection under varying data skew, query selectivity, and storage conditions, validated on real point sets.