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arxiv: 2604.18227 · v1 · submitted 2026-04-20 · 💻 cs.LG

FSEVAL: Feature Selection Evaluation Toolbox and Dashboard

Muhammad Rajabinasab , Arthur Zimek This is my paper

Pith reviewed 2026-05-10 05:08 UTC · model grok-4.3

classification 💻 cs.LG
keywords feature selectionevaluation toolboxvisualization dashboardmachine learningstandardizationsupervised learningunsupervised learningdimensionality reduction
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The pith

FSEVAL supplies a unified toolbox and dashboard for standardized evaluation of feature selection algorithms across supervised and unsupervised settings.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents FSEVAL as a new software resource that bundles evaluation metrics and visualization tools into one accessible package. Feature selection aims to isolate informative features while retaining interpretability, yet different studies currently apply incompatible metrics and procedures that hinder direct comparisons. By offering a single standardized environment, FSEVAL seeks to let researchers run broad, consistent tests without writing bespoke code each time. A sympathetic reader would expect this to reduce wasted effort on repeated baseline implementations and to produce more reliable rankings of which algorithms perform best under which conditions.

Core claim

FSEVAL is a feature selection evaluation toolbox accompanied with a visualization dashboard, with the goal to make it easy to comprehensively evaluate feature selection algorithms. It 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.

What carries the argument

The FSEVAL toolbox and dashboard, which integrates evaluation metrics, visualization components, and support for both supervised and unsupervised feature selection into a single workflow.

If this is right

  • Researchers gain a ready-made way to run and compare algorithms without rebuilding evaluation pipelines from scratch.
  • Direct, apples-to-apples comparisons become feasible between supervised and unsupervised feature selection methods.
  • The dashboard makes it straightforward to inspect how different metrics behave on the same data and algorithms.
  • Published results can cite the same evaluation protocol, reducing ambiguity when later studies attempt to reproduce or extend earlier findings.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Widespread adoption could turn FSEVAL into a de-facto benchmark that future papers are expected to use, similar to how other standardized toolkits have shaped their fields.
  • The existence of one reference implementation might surface previously hidden inconsistencies in how metrics are computed across different libraries.
  • Future extensions could add domain-specific modules or automated reporting that further lowers the barrier for non-expert users.

Load-bearing premise

That the variety of existing evaluation practices is mainly a problem of missing standardization rather than fundamental differences in what each setting requires.

What would settle it

A head-to-head comparison in which independent teams apply FSEVAL to the same set of algorithms and still obtain substantially different performance rankings or conclusions.

Figures

Figures reproduced from arXiv: 2604.18227 by Arthur Zimek, Muhammad Rajabinasab.

Figure 1
Figure 1. Figure 1: The FSEval Dashboard: An interactive environment for analytical insights and comparisons on feature selection algorithms. A critical feature of the dashboard is its ability to generate high-fidelity, publication￾ready figures [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Publication-ready line plot generated by the dashboard, demonstrating the per￾formance trajectory and variance of multiple algorithms. 2500 5000 7500 10000 12500 15000 17500 20000 Number of Features 10 4 10 3 10 2 10 1 10 0 10 1 10 2 10 3 Runtime (s) Variance Correlation Laplacian Random VCSDFS LIDFS SOGFS LLSRFS SCFS MCFS [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Scalability analysis showing runtime (seconds) against the number of features. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Scalability analysis showing runtime (seconds) against the number of instances. [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces FSEVAL, a feature selection evaluation toolbox accompanied by a visualization dashboard. Its central claim is that the tool supplies a standardized, unified interface for comprehensive evaluation and visualization of feature selection algorithms across supervised and unsupervised settings, thereby simplifying extensive comparisons for researchers.

Significance. If the implemented toolbox indeed covers a representative range of algorithms and metrics with reproducible interfaces, it could help reduce ad-hoc evaluation practices in feature selection research and improve cross-study comparability, which remains a practical bottleneck in the field.

major comments (2)
  1. [Abstract] Abstract: the manuscript states the goal of providing a 'standardized, unified' toolbox but supplies no implementation details, list of supported feature selection methods, covered evaluation metrics for supervised versus unsupervised cases, or any validation experiments. This information is load-bearing for assessing whether the central claim of comprehensiveness and standardization holds.
  2. [Full manuscript] The paper contains no description of the dashboard's visualization capabilities, input/output formats, or example workflows, making it impossible to evaluate usability or coverage of the claimed supervised/unsupervised settings.
minor comments (1)
  1. [Abstract] The phrasing 'involved with discriminating redundant features from informative ones' is slightly awkward; consider 'involves distinguishing redundant features from informative ones' for improved readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript introducing FSEVAL. We agree that the current version lacks sufficient implementation details to fully support the claims of standardization and comprehensiveness. We will perform a major revision to address these points by expanding the abstract, adding dedicated sections on the toolbox and dashboard, and including supporting details and examples.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the manuscript states the goal of providing a 'standardized, unified' toolbox but supplies no implementation details, list of supported feature selection methods, covered evaluation metrics for supervised versus unsupervised cases, or any validation experiments. This information is load-bearing for assessing whether the central claim of comprehensiveness and standardization holds.

    Authors: We acknowledge this limitation in the current manuscript. The initial submission provided only a high-level overview. In the revised version, we will expand the abstract to include key implementation details, a list of supported feature selection methods (covering representative supervised methods such as ReliefF, mRMR, and LASSO, and unsupervised methods such as Laplacian Score and SPEC), the evaluation metrics used in each setting (e.g., classification accuracy and F1-score for supervised; clustering metrics like silhouette score and normalized mutual information for unsupervised), and a brief summary of validation experiments demonstrating reproducibility and comparability across algorithms. revision: yes

  2. Referee: [Full manuscript] The paper contains no description of the dashboard's visualization capabilities, input/output formats, or example workflows, making it impossible to evaluate usability or coverage of the claimed supervised/unsupervised settings.

    Authors: We agree that these elements are missing from the current text. The revised manuscript will add a dedicated section describing the dashboard's visualization capabilities (including interactive plots for feature rankings, performance heatmaps, and comparison charts), input/output formats (support for CSV, NumPy arrays, and scikit-learn compatible objects), and concrete example workflows illustrating end-to-end usage in both supervised classification and unsupervised clustering scenarios. This will enable readers to assess usability and coverage directly. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is a software engineering contribution describing the FSEVAL toolbox and dashboard for feature selection evaluation. It contains no mathematical derivations, equations, predictions, fitted parameters, or theoretical claims that could form a derivation chain. The central assertion is simply that the provided implementation offers a standardized interface, which rests on code correctness and coverage rather than any self-referential logic or self-citation load-bearing step. No circularity patterns apply.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper is a software-tool proposal containing no mathematical derivations, fitted parameters, or postulated entities.

pith-pipeline@v0.9.0 · 5413 in / 910 out tokens · 35336 ms · 2026-05-10T05:08:24.695628+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

18 extracted references · 2 canonical work pages · 1 internal anchor

  1. [1]

    Journal of Machine Learning Research , volume =

    Guyon, Isabelle and Elisseeff, André , title =. Journal of Machine Learning Research , volume =

  2. [2]

    and Tang, Jiliang and Liu, Huan , title =

    Li, Jundong and Cheng, Kewei and Wang, Suhang and Morstatter, Fred and Trevino, Robert P. and Tang, Jiliang and Liu, Huan , title =. ACM Computing Surveys , volume =

  3. [3]

    Algorithms , volume=

    A Feature Selection Algorithm Performance Metric for Comparative Analysis , author=. Algorithms , volume=. 2021 , publisher=

    2021

  4. [4]

    International Conference on Similarity Search and Applications (SISAP 2024) , pages=

    A Dynamic Evaluation Metric for Feature Selection , author=. International Conference on Similarity Search and Applications (SISAP 2024) , pages=. 2024 , organization=

    2024

  5. [5]

    Proceedings of the 2025 SIAM International Conference on Data Mining (SDM 2025) , year=

    Metrics for Inter-Dataset Similarity with Example Applications in Synthetic Data and Feature Selection Evaluation , author=. Proceedings of the 2025 SIAM International Conference on Data Mining (SDM 2025) , year=

    2025

  6. [6]

    Journal of Machine Learning Research , volume=

    On the Stability of Feature Selection Algorithms , author=. Journal of Machine Learning Research , volume=

  7. [7]

    Proceedings of the 25th IASTED International Multi-Conference: Artificial Intelligence and Applications , pages=

    A stability index for feature selection , author=. Proceedings of the 25th IASTED International Multi-Conference: Artificial Intelligence and Applications , pages=

  8. [8]

    On lines and planes of closest fit to systems of points in space , author=

    LIII. On lines and planes of closest fit to systems of points in space , author=. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science , volume=. 1901 , publisher=

    1901

  9. [9]

    Biological Cybernetics , volume=

    Auto-association by multilayer perceptrons and singular value decomposition , author=. Biological Cybernetics , volume=

  10. [10]

    Journal of Machine Learning Research , volume=

    Visualizing data using t-SNE , author=. Journal of Machine Learning Research , volume=

  11. [11]

    UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

    UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction , author=. arXiv preprint arXiv:1802.03426 , year=

    work page internal anchor Pith review arXiv

  12. [12]

    Journal of Machine Learning Research , volume=

    Feature selection for unsupervised learning , author=. Journal of Machine Learning Research , volume=

  13. [13]

    IEEE transactions on information theory , volume=

    Least squares quantization in PCM , author=. IEEE transactions on information theory , volume=. 1982 , publisher=

    1982

  14. [14]

    Journal of Machine Learning Research , volume=

    Statistical comparisons of classifiers over multiple data sets , author=. Journal of Machine Learning Research , volume=

  15. [15]

    featsel: A framework for benchmarking of feature selection algorithms and cost functions , journal =

    Benedito José. featsel: A framework for benchmarking of feature selection algorithms and cost functions , journal =. 2017 , issn =. doi:https://doi.org/10.1016/j.softx.2017.01.002 , Xurl =

    work page doi:10.1016/j.softx.2017.01.002 2017

  16. [16]

    Pattern Recognition , volume=

    Feature selection based on rough diversity entropy , author=. Pattern Recognition , volume=. 2026 , publisher=

    2026

  17. [17]

    Pattern Recognition , volume=

    Online multi-label streaming feature selection by affinity significance, affinity relevance and affinity redundancy , author=. Pattern Recognition , volume=. 2026 , publisher=

    2026

  18. [18]

    Computers and Electrical Engineering , volume=

    EnFeSTDroid: Ensembled feature selection techniques based Android malware detection , author=. Computers and Electrical Engineering , volume=. 2026 , publisher=

    2026