Combining correlation-based feature selection with genetic algorithm tuning on random forest achieves 88.40% accuracy for software fault prediction, an 18% gain over baselines without selection or tuning.
Machine learning-based test case prioritization using hyperparameter optimization,
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A Feature-Driven Framework for Software Fault Prediction
Combining correlation-based feature selection with genetic algorithm tuning on random forest achieves 88.40% accuracy for software fault prediction, an 18% gain over baselines without selection or tuning.