PONE-D-25-38473Rare Event Detection by Progressive Clustering UndersamplingPLOS ONE

Dear Dr. Abuzeid,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The reviewers have completed the reviewing, and you could find the comments by the reviewers. It is required to enhance its scientific rigor, clarity, and completeness before it can be considered for acceptance. The key revisions needed are:

Methodological and Experimental Rigor: It is necessary to elaborate on the specific implementation details of the PCU algorithm, including the criteria for selecting clustering algorithms, determining key parameters (e.g., the "rupture" point), and conducting parameter sensitivity and computational complexity analyses. Please also supplement the description of the experimental setup and incorporate statistical significance tests to strengthen the credibility of the comparative results.

Depth of Analysis and Discussion: The manuscript requires a dedicated subsection to systematically discuss the limitations of the proposed method, potential failure scenarios, computational costs, and its generalizability to other domains (e.g., finance, healthcare). It is also recommended to include a summary table clearly outlining the advantages and disadvantages of related studies.

Presentation and Language Quality: Please enhance the resolution and clarity of the figures (Figures 1-4) and ensure the "Graphical Abstract" mentioned is either included or all references to it are removed. Furthermore, a thorough revision of the text is needed to address language issues, reference formatting (which requires updating), and consistency in terminology (e.g., the "Semi-Guided" branch).

I look forward to receiving your revised manuscript accompanied by a detailed point-by-point response letter. I hope the reviewers' feedback is useful to you.

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Kind regards,

Hui Li

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: this study explores various resampling techniques and introduces a novel method called Progressive Clustering Undersampling (PCU). This technique removes negative instances that are distant from positive ones. PCU was compared with eight common undersampling and two oversampling techniques, consistently outperforming them on highly imbalanced and noisy datasets. The workflow demonstrates that rare anomalies can be effectively predicted using unsupervised methods based on frequency-driven decision boundaries. Progressive clustering ultimately identifies clusters with the highest concentration of positive instances. These delineated clusters are then saved by supervised models and used in the preparatory phase before prediction. The proposed method produces two outputs: one optimized for a high F1- score and the other for high precision. Overall, this approach presents a promising solution for identifying rare anomalies in complex, imbalanced data environments.

Good work keeps up

But some comments are needed?

all of them are submitted to the editor

Reviewer #2: Overall Evaluation

This manuscript presents a novel approach named Progressive Clustering Undersampling (PCU) for detecting rare events in highly imbalanced datasets. The study is well-motivated and addresses an important challenge in machine learning and data-driven anomaly detection. The integration of clustering-based unsupervised learning with progressive undersampling and supervised refinement is innovative and potentially impactful.

The manuscript is generally well-written, methodologically sound, and supported by comprehensive experiments on both real-world and synthetic datasets. However, several key areas require major revisions to improve clarity, reproducibility, and scientific rigor before the paper can be accepted for publication.

Major Comments

Methodological Clarity and Reproducibility

The PCU algorithm is interesting but described in a largely conceptual way. The pseudocode in Algorithm 1 lacks specific implementation details such as:

How clustering algorithms are selected or switched between stages.

Criteria used for determining the “rupture” point.

Parameter tuning strategies for clustering algorithms.

Please expand this section with more formal definitions, computational complexity, and parameter sensitivity analysis.

Comparative Evaluation

While the paper includes comparisons with several resampling methods, it would strengthen the work to include statistical significance tests (e.g., t-test or Wilcoxon) across repeated runs.

Please clarify whether the same random seed or data splits were used across all methods to ensure fairness.

Generalizability

The petroleum dataset is well described, but the method’s generalizability beyond this specific application (e.g., finance, healthcare, cybersecurity) should be better discussed.

Consider adding one more benchmark dataset from a different domain to show robustness.

Figures and Visualizations

Figures 1–4 are valuable but require higher resolution and clearer legends.

The “Graphical Abstract” mentioned in the text is missing from the submission. Please include it or remove all references to it.

Ablation Study

Since PCU combines unsupervised clustering and supervised refinement, it is important to show the contribution of each step individually (e.g., clustering-only, supervised-only, and combined).

Language and Structure

Some sections (e.g., Introduction and Related Work) are overly descriptive. Please focus more on critical comparisons and concise synthesis.

Check for consistency in reference formatting (e.g., DOI style and numbering).

Minor Comments

Typographical and formatting errors occur in several places (e.g., “undersam-pling,” “pos-itive”). Please revise carefully.

Include clear variable definitions when first introduced in equations or pseudocode.

The term “Semi-Guided” and “Fully-Guided” branches should be formally defined and consistently referenced.

Ensure all URLs in the references are correctly formatted and accessible.

Clarify whether “noise” in the Two Moons dataset refers to label noise or feature noise.

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Reviewer #1: No

Reviewer #2: No

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Rare Event Detection by Progressive Clustering Undersampling

PONE-D-25-38473R1

Dear Dr. Abuzeid,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Hui Li

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This study presents Capturing rare events in severely imbalanced datasets is challenging, as the learning and optimization processes are often biased toward the majority class. To address this issue, this study explores various resampling techniques and introduces a novel method called Progressive Clustering Undersampling (PCU). This technique removes negative instances that are distant from positive ones. PCU was compared with eight common undersampling and two oversampling techniques, consistently outperforming them on highly imbalanced and noisy datasets. The workflow demonstrates that rare anomalies can be effectively predicted using unsupervised methods based on frequency-driven decision boundaries. Progressive clustering ultimately identifies clusters with the highest concentration of positive instances. These delineated clusters are then saved by supervised models and used in the preparatory phase before prediction. The proposed method produces two outputs: one optimized for a high F1- score and the other for high precision. Overall, this approach presents a promising solution for identifying rare anomalies in complex, imbalanced data environments.

Good work keeps up

Reviewer #2: Recommendation: Accept with Minor Revisions

Overall Assessment

The authors have done an excellent job addressing the concerns raised in the previous round of reviews. The manuscript has been substantially improved in terms of clarity, methodological detail, reproducibility, and discussion of limitations. The introduction of subsection 5.1 (limitations/challenges/contributions/structure) and 5.2 (experimental setup, complexity, parameter choices) directly resolves many of the earlier criticisms. Figures have been upgraded, the graphical abstract is now properly placed, and additional experiments on noise variations strengthen the claims. The responses to both reviewers are thoughtful, transparent, and courteous.

The proposed Progressive Clustering Undersampling (PCU) method is novel, well-motivated for highly imbalanced and noisy settings, and the empirical evidence (across petroleum and Two Moons datasets) convincingly shows superior performance over a wide range of established resampling baselines.

The paper is now suitable for publication in PLOS ONE after only minor revisions.

Minor Revisions Required

Abstract – Quantitative Summary

The abstract states that PCU “consistently outperform[s]” other methods but provides no quantitative indication. While the authors correctly note character limits, consider adding a brief quantitative remark, e.g.:

“achieving up to X% higher F1-score and Y% higher precision than the next-best resampling technique on highly imbalanced noisy datasets.”

Even an approximate range or “substantial improvements” would help readers quickly gauge impact.

Conclusion – Strengthen Quantitative Takeaway

The conclusion mentions KNN results but could more prominently highlight the best overall gains of PCU (e.g., from Tables/Figures) across classifiers and datasets. A single sentence summarizing the magnitude of improvement would reinforce the contribution.

Statistical Significance

The authors added multiple runs and report variability, which is appreciated. For the final version, please consider adding a brief statement (or footnote to tables) indicating whether differences were statistically significant (e.g., via paired t-test or Wilcoxon on the repeated runs). This is not mandatory for PLOS ONE but would further strengthen the comparative claims.

Generalizability Discussion

The added benchmark discussion and limitation paragraph are helpful. To further improve readability, consider moving or duplicating the key sentence about expected beneficial domains (finance, healthcare, cybersecurity) into the Conclusion for emphasis.

Minor Editorial / Consistency Issues

Check for any remaining automatic hyphenations (e.g., “undersam-pling”, “pos-itive”) and disable if possible or manually correct.

Ensure all new references added in this revision follow the exact PLOS ONE style (especially DOI formatting).

In the text, “rupture point” is now clearer, but consider using a more standard term (e.g., “stopping criterion” or “termination condition”) or defining it explicitly on first use for broader accessibility.

Graphical Abstract

Confirm that the graphical abstract is uploaded as a separate high-resolution file in the final submission system, as required by PLOS ONE.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

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