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FSEVAL: Feature Selection Evaluation Toolbox and Dashboard
[Submitted on 20 Apr 2026 (v1), last revised 23 Aug 2026 (this v · 2026-05-28 · via cs.LG updates on arXiv.org

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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.

Submission history

From: Muhammad Rajabinasab [view email]
[v1] Mon, 20 Apr 2026 13:11:25 UTC (179 KB)
[v2] Tue, 26 May 2026 19:46:57 UTC (196 KB)
[v3] Fri, 19 Jun 2026 11:52:15 UTC (196 KB)
[v4] Sun, 23 Aug 2026 09:14:54 UTC (196 KB)