FSEVAL: Feature Selection Evaluation Toolbox and Dashboard

TL;DR

FSEVAL is a comprehensive toolbox and dashboard designed to facilitate standardized evaluation and visualization of feature selection algorithms in machine learning and data mining.

Contribution

It introduces a unified platform for evaluating feature selection methods, enhancing comparability and interpretability of results.

Findings

Provides a standardized evaluation framework

Includes visualization tools for better analysis

Simplifies comprehensive assessment of feature selection algorithms

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.