-->PONE-D-25-66864-->-->Ensemble learning-based online sequential pre-interference extreme learning for concept drifting and class imbalanced data streams-->-->PLOS One

Dear Dr. Wen,

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Academic Editor

PLOS One

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This work is supported by the Fujian Provincial Natural Science Foundation Project (2023J011015, 2022J011179, 2024J01888), Ministry of Education Industry-Academia Collaborative Education Project(231102311285117), Putian Science and Technology Plan Project(2024NJJ009, 2023GJGZ003,

2024

GZ2001PTXY17), Putian University Graduate Research and Innovation Project(yjs2024054), Fujian Province Science and Technology Special Envoy Project(F2022KTP027, F2024KTP086), National Natural Science Foundation of China (grant number 52577115), Putian High end Equipment Industry Technology Research Institute(2023GJGZ002).

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This work is supported by the Fujian Provincial Natural Science Foundation Project (2023J011015, 2022J011179, 2024J01888), Ministry of Education Industry-Academia Collaborative Education Project(231102311285117), Putian Science and Technology Plan Project(2024NJJ009, 2023GJGZ003,

2024

GZ2001PTXY17), Putian University Graduate Research and Innovation Project(yjs2024054), Fujian Province Science and Technology Special Envoy Project(F2022KTP027, F2024KTP086), National Natural Science Foundation of China (grant number 52577115), Putian High end Equipment Industry Technology Research Institute(2023GJGZ002).

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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

Reviewer #3: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: 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

Reviewer #3: 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

Reviewer #3: 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: Revised Review Comments�

This paper proposes an online learning algorithm called OS-PIELM, which enhances the discriminative power of data stream features by introducing an interference layer between the input and hidden layers of the traditional OS-ELM for nonlinear kernel mapping. The method also integrates an adaptive concept drift detection mechanism based on G-means and a dynamic weighting strategy, enabling it to simultaneously handle class imbalance and concept drift problems. Overall, the methodology is highly innovative, but it still has the following shortcomings:

1. What is the relationship between the proposed concept drift and class imbalance? Is it the result of problem A + problem B, or an intersection of problem A and problem B? This needs to be explained in detail.

2. Supplement the description of Fig. 3 (Problem: The descriptions of different figures are completely identical).

3. The ordinate of Fig. 6 does not match the description.

4. The punctuation in "comparison among and other methods" is incorrect.

5. Supplement the description of the comparative model.

6. Add an explanation of "virtual drift," etc.

7. Supplement the width of the drift.

8. The authors need to supplement the discussion of the broader field of stream learning, particularly regarding the current state of research in online learning, and point out the connections and differences between the proposed method and related works to better illustrate the work presented in this paper. This includes, but is not limited to, the following outstanding works:

Online Learning from Mix-typed, Drifted, and Incomplete Streaming Features (TKDD)

Online Semi-supervised Learning with Mix-Typed Streaming Features (AAAI)

Online Learning for Data Streams With Incomplete Features and Labels (TKDE)

Reviewer #2: Reviewer Comments

1. Inconsistent Terminology Usage

The key terms pre-interference layer and interference layer are used interchangeably throughout the manuscript to refer to the same structural component added to the OS-ELM model. Additionally, the concept of class imbalance is described with two inconsistent expressions: class imbalanced and category imbalance. Such terminological inconsistency violates the basic norms of academic writing and may cause confusion for readers in understanding the core design and research object of the study.

2. Grammatical Errors and Inadequate Proofreading

The manuscript contains multiple grammatical errors and spelling mistakes, which reflect a lack of careful proofreading. For example, the conjunction redundancy in the sentence "Although the interference layer... but the class labels..." (the simultaneous use of Although and but violates English grammatical rules); spelling errors such as Performce (correct: Performance) and ndr rves (correct: curves) in the text and figure captions. These issues significantly reduce the readability and formality of the manuscript.

3. Insufficient Interpretation of Key Figures

Key experimental figures (Figure 5, Figure 6, Figure 7) are only briefly referenced with a general conclusion in the text, without detailed data-driven interpretation and analysis. The manuscript fails to elaborate on the specific trends, critical inflection points, and statistical characteristics of the experimental results presented in these figures, nor does it link the figure data to the core research hypotheses and algorithm performance conclusions of the study. This makes the experimental results lack sufficient empirical support and weakens the persuasiveness of the research findings.

4. Superficial Review of Existing Literature and Unclear Research Gaps

The review of existing research merely lists the improved methods of OS-ELM for data stream learning in a descriptive manner, without systematically sorting out and summarizing the core research challenges in the field of class-imbalanced data stream learning with concept drift (e.g., poor coupling of multiple functional modules, inadequate handling of multi-class classification scenarios, weak noise resistance of algorithms). More importantly, the manuscript does not explicitly establish the corresponding relationship between the identified research gaps and the innovative points of the proposed EOS-PIELM algorithm, which makes it difficult for readers to recognize the theoretical and practical contributions of this study relative to the existing literature.

Reviewer #3: 1. Overall Evaluation

This manuscript proposes an online ensemble learning framework (EOS-PIELM) designed to simultaneously address two major challenges in data stream learning: concept drift and class imbalance. The authors extend OS-ELM by introducing:

A pre-interference nonlinear mapping layer (OS-PIELM),

A Gmean-based concept drift detection mechanism with an adaptive forgetting factor,

A dynamic class-weighting strategy,

An online ensemble framework with performance-based voting.

The topic is timely and practically relevant, especially for real-world streaming scenarios where distribution shifts and imbalance frequently co-occur. The manuscript is generally well-structured, and the experimental results demonstrate consistent performance improvements across synthetic and real-world datasets.

Overall, the work presents a meaningful incremental advancement in online ELM-based stream learning and is suitable for publication after addressing several clarifications and improvements.

2. Strengths of the Manuscript

2.1 Practical Relevance

The simultaneous treatment of concept drift and class imbalance reflects realistic industrial and real-time data environments (e.g., weather forecasting and electricity pricing). The proposed framework directly targets these combined challenges.

2.2 Systematic Framework Design

The algorithm integrates multiple components coherently:

Nonlinear feature enhancement via the pre-interference layer,

Drift detection grounded in Gmean performance,

Adaptive forgetting factor adjustment,

Cost-sensitive dynamic reweighting,

Ensemble voting with classifier-specific correction factors.

The overall architecture is logically constructed and technically sound.

2.3 Empirical Performance

Experimental results show:

Consistently higher Gmean values compared to OS-ELM variants,

Strong robustness under varying imbalance ratios,

Stable behavior on both abrupt and gradual drift,

Improved performance on real-world datasets (Weather and Elec).

The inclusion of multiple evaluation metrics (Accuracy, Recall, Specificity, Gmean, D(Rec, Spe)) strengthens the empirical analysis.

3. Suggestions for Improvement

While the manuscript is promising, several points could be clarified or strengthened to improve readability and rigor.

3.1 Clarify the Role of the Pre-Interference Layer

The pre-interference layer is a key contribution. However, its theoretical motivation could be elaborated further:

Is ϕ(x) fixed or randomly initialized?

How does this differ from standard kernel ELM or random feature mapping?

Does it increase representational capacity theoretically?

A brief discussion on its theoretical interpretation would enhance clarity.

3.2 Drift Detection Justification

The Gmean-based drift detection mechanism is reasonable and well-motivated for imbalanced data. However:

A short comparison with classical drift detectors (e.g., DDM or ADWIN) would strengthen the positioning.

A brief discussion on false alarm robustness would be helpful.

Even a conceptual comparison (without additional experiments) would improve completeness.

3.3 Statistical Significance

The performance improvements are consistent, but adding a statistical significance test (e.g., Wilcoxon signed-rank test across datasets) would increase the scientific rigor.

3.4 Computational Complexity Discussion

Since the framework introduces ensemble learning and adaptive updates, it would be beneficial to include:

A short time-complexity discussion,

A comparison with standard OS-ELM in terms of computational overhead.

Even a qualitative complexity analysis would suffice.

3.5 Language and Minor Editorial Improvements

The manuscript is generally understandable but would benefit from:

Minor grammatical corrections,

Slight refinement of phrasing in the methodology section,

Clarification of certain formula transitions.

A light professional English editing pass is recommended.

4. Conclusion

This work presents a well-designed and practically motivated extension of OS-ELM for handling both concept drift and class imbalance in data streams. The integration of nonlinear feature mapping, adaptive forgetting, and dynamic class reweighting within an ensemble framework is coherent and experimentally validated.

The manuscript demonstrates clear performance advantages over several baseline methods and provides sufficient empirical support.

With minor clarifications and modest revisions to improve theoretical explanation and presentation quality, the paper would be suitable for publication.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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-->PONE-D-25-66864R1-->-->Ensemble learning-based online sequential pre-interference extreme learning for concept drifting and class imbalanced data streams-->-->PLOS One

Dear Dr. Wen,

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.-->-->

Please submit your revised manuscript by Jul 13 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:-->

• A letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
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• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

-->

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We look forward to receiving your revised manuscript.

Kind regards,

Zeheng Wang

Academic Editor

PLOS One

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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 #3: All comments have been addressed

Reviewer #4: (No Response)

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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: (No Response)

Reviewer #3: Yes

Reviewer #4: Yes

**********

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

Reviewer #1: (No Response)

Reviewer #3: Yes

Reviewer #4: Yes

**********

-->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: (No Response)

Reviewer #3: Yes

Reviewer #4: Yes

**********

-->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: (No Response)

Reviewer #3: Yes

Reviewer #4: 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: (No Response)

Reviewer #3: (No Response)

Reviewer #4: The paper is interesting and the topic is relevant, but I still feel the manuscript needs a stronger positioning in the current literature. Right now, the introduction talks about concept drift, OS-ELM, and class imbalance, but the review still feels a bit selective and not fully up to date. In particular, the authors should cite and discuss recent work such as Li et al. (2024), “Suboptimal capability of individual machine learning algorithms in modeling small-scale imbalanced clinical data of local hospital,” PLOS ONE 19(2): e0298328. That paper is not about data streams directly, but it is highly relevant because it shows that standard individual ML models can perform poorly on small-scale, highly imbalanced real-world data, which supports the motivation for more robust imbalance-aware and ensemble-based methods. Ignoring this kind of recent evidence makes the paper’s motivation look less complete.

I also think the technical novelty needs to be explained more carefully. The proposed “pre-interference layer” seems close to nonlinear kernel mapping or random feature transformation, so the authors should clearly say how it is different from kernel ELM, random feature ELM, or other feature-mapping-based online learners. Some parts of the manuscript are still not very clear: for example, the abstract and method description use long sentences with grammar problems, Algorithm 1 is too vague to reproduce the method, and the explanation around the Gmean-based drift detector could better distinguish real improvement from just adding several known modules together. Since the paper claims to jointly handle concept drift and class imbalance, the authors should more directly connect each experimental result to that claim, rather than only reporting that EOS-PIELM performs better overall.

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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 #3: No

Reviewer #4: No

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-->

Ensemble learning-based online sequential pre-interference extreme learning for concept drifting and class imbalanced data streams

PONE-D-25-66864R2

Dear Dr. Wen,

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,

Zeheng Wang

Academic Editor

PLOS One

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