A unified, publicly released benchmarking framework evaluates 17 feature selection methods across 10 Android malware datasets; LASSO, RFE, and SigAPI come out most consistent, while PCA, ReliefF, and SigPID lag.
Importance of Features Selection, Attributes Selection, Challenges and Future Directions for Medical Imaging Data: A Review,
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MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation
A unified, publicly released benchmarking framework evaluates 17 feature selection methods across 10 Android malware datasets; LASSO, RFE, and SigAPI come out most consistent, while PCA, ReliefF, and SigPID lag.