PAIR-CI restores calibration to conditional independence testing under missing data by using paired permutations that force imputation error to cancel in the loss difference, together with a consistent variance estimator that unifies cross-validation and imputation uncertainty.
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Adaptive MSD-Splitting improves C4.5 and Random Forest performance on skewed data by adjusting standard deviation multipliers for discretization while retaining linear time complexity.
citing papers explorer
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PAIR-CI: Calibrated Conditional Independence Testing for Causal Discovery with Incomplete Data
PAIR-CI restores calibration to conditional independence testing under missing data by using paired permutations that force imputation error to cancel in the loss difference, together with a consistent variance estimator that unifies cross-validation and imputation uncertainty.
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Adaptive MSD-Splitting: Enhancing C4.5 and Random Forests for Skewed Continuous Attributes
Adaptive MSD-Splitting improves C4.5 and Random Forest performance on skewed data by adjusting standard deviation multipliers for discretization while retaining linear time complexity.