Conditional mutual information bounds ideal prediction errors for feature subsets and supplies a stopping condition for greedy selection algorithms.
Lin- ear correlation-based feature selection for network intrusion detection model,
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Feature Selection via Mutual Information: New Theoretical Insights
Conditional mutual information bounds ideal prediction errors for feature subsets and supplies a stopping condition for greedy selection algorithms.