BMNB uses blended likelihood with tunable alpha and post-processing calibration to achieve DI close to 1 and improved EOD on three standard fairness datasets while preserving efficiency.
Fairness-aware naive Bayes classifier for data with multiple sensitive features,
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A Blended Likelihood Approach for Achieving Fairness Using Naive Bayes
BMNB uses blended likelihood with tunable alpha and post-processing calibration to achieve DI close to 1 and improved EOD on three standard fairness datasets while preserving efficiency.