Greedy recursive elimination almost consistently beats one-shot global feature ranking on supervised metrics across five selectors, at higher computational cost.
FSEVAL: Feature Selection Evaluation Toolbox and Dashboard
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abstract
Feature selection is a fundamental machine learning and data mining task, involved with discriminating redundant features from informative ones. It is an attempt to address the curse of dimensionality by removing the redundant features, while unlike dimensionality reduction methods, preserving explainability. Feature selection is conducted in both supervised and unsupervised settings, with different evaluation metrics employed to determine which feature selection algorithm is the best. In this paper, we propose FSEVAL, a feature selection evaluation toolbox accompanied with a visualization dashboard, with the goal to make it easy to comprehensively evaluate feature selection algorithms. FSEVAL aims to provide a standardized, unified, evaluation and visualization toolbox to help the researchers working in the field, conduct extensive and comprehensive evaluation of feature selection algorithms with ease.
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cs.LG 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
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An Empirical Study of Feature Selection Granularity
Greedy recursive elimination almost consistently beats one-shot global feature ranking on supervised metrics across five selectors, at higher computational cost.