Advancing Interaction-Sensitive Feature Selection: Novel Relief-Based Algorithms, Expanded Comparisons, and Recommendations for Biomedical Data Mining
As a precursor to high-dimensional biomedical data modeling, reliable feature selection can reduce computational expense, improve modeling performance, and yield simpler, more interpretable models. However, most filter-based feature selection methods struggle to detect feature interactions, while wrapper or embedded feature selection methods are computationally expensive. Relief-based algorithms (RBAs) are filter methods that are sensitive to feature interactions while mitigating these other limitations. This study (1) refactors, optimizes, and expands the scikit-rebate Python package with exi
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- PossiblePossibly related (embedding) · 47%New SHAP-McNemar feature selection method boosts machine learning credit risk models - Bioengineer.org →
- LinkedLinked via arxiv author · 85%Kia Kazemi-Nia →
“Advancing Interaction-Sensitive Feature Selection: Novel Relief-Based Algorithms, Expanded Comparisons, and Recommendati”
- LinkedLinked via arxiv author · 85%Harsh Bandhey →
“Advancing Interaction-Sensitive Feature Selection: Novel Relief-Based Algorithms, Expanded Comparisons, and Recommendati”
- LinkedLinked via arxiv author · 85%Philip J. Freda →
“Advancing Interaction-Sensitive Feature Selection: Novel Relief-Based Algorithms, Expanded Comparisons, and Recommendati”
- LinkedLinked via arxiv author · 85%Ryan J. Urbanowicz →
“Advancing Interaction-Sensitive Feature Selection: Novel Relief-Based Algorithms, Expanded Comparisons, and Recommendati”