FSEVAL: Feature Selection Evaluation Toolbox and Dashboard
Provides a unified evaluation framework for researchers working on feature selection, but is incremental as it combines existing metrics and visualization.
FSEVAL is a toolbox and dashboard for standardized evaluation of feature selection algorithms, aiming to simplify comprehensive comparisons. No concrete results are reported.
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.