FSEVAL: Feature Selection Evaluation Toolbox and Dashboard
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. F...
| Authors | Muhammad Rajabinasab, Arthur Zimek |
|---|---|
| Primary category | cs.LG |
| Published | 20 April 2026 |
| Last updated at source | 23 August 2026 |
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