PONE-D-24-38797Addressing Imbalanced Data Classification with Cluster-Based Reduced Noise SMOTEPLOS ONE

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Academic Editor

PLOS ONE

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Additional Editor Comments:

Major Revision

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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: Partly

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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: No

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: The authors have done well but still, some points need to be addressed which increases the readability and look of the manuscript better.

Here are some points

1- The author mentions which dataset results have better accuracy in the abstract.

2- The literature review has two subheadings current research and research gap.

3- The author needs to read the following manuscript and add some applications of Machine learning in different fields in the literature review ”Enhancing software defect prediction: a framework with improved feature selection and ensemble machine learning” “Impact of 3G and 4G technology performance on customer satisfaction in the telecommunication industry” Software defect prediction using an intelligent ensemble-based model.

4- Future work must be a section

5- The author needs to add a table before the conclusion in which results have been compared with some other studies

6- There must be a discussion section at the end

7- The author needs to add tables and figures in the Manuscript rather than down the manuscript so that there will be a better look and easier for the reader

8- What are the issues of the imbalanced dataset and how was it resolved

9- Write the advantages and disadvantages of the proposed method.

10- What are the disadvantages of the Synthetic Minority Oversampling Technique

11- The conclusion must be concise as abstract

Reviewer #2: Overall the paper seems interesting. The topic is valid and well organized. However, the following recommendations are needs to be addressed in order to improve the quality of the papers.

1. The abstract needs more improvement, the authors should mention the results and improvement than existing methods.

2.The problem statement and motivation of the paper needs to be clearly mention to provide a clear background to the readers.

2. The authors should provide the main contributions in points at the end of introduction section.

3. I appreciate the valuable use of machine learning in the model. For the readers concerns, i suggest adding/citing the recent computational models related machine learning and oversampling such as AIPs-DeepEnC-GA, StackedEnC-AOP, DeepAVPTPPred,

iAFPs-Mv-BiTCN, AIPs-SnTCN and Deepstacked-AVPs in order to provide useful information to the readers .

4. How the authors handle the overfitting and generalization of the proposed model.

5. The authors should clearly mention the future directions and real life applications of the proposed study.

6. What are the limitations of the proposed model.

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Reviewer #1: Yes: Tehseen Mazhar

Reviewer #2: No

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Addressing Imbalanced Data Classification with Cluster-Based Reduced Noise SMOTE

PONE-D-24-38797R1

Dear Dr. Hajizadeh,

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,

Agbotiname Lucky Imoize

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Accept.

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 #2: All comments have been addressed

Reviewer #3: (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 #2: Yes

Reviewer #3: Yes

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

Reviewer #2: Yes

Reviewer #3: 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 #2: Yes

Reviewer #3: 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 #2: Yes

Reviewer #3: Yes

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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 #2: The required concerns are successfully incorporated by the authors and now the paper quality have improved.i recommend to accept the paper.

Reviewer #3: The manuscript titled "Addressing Imbalanced Data Classification with Cluster-Based Reduced Noise SMOTE" addresses a critical issue in machine learning: handling imbalanced datasets. The authors propose a novel method, Cluster-Based Reduced Noise SMOTE (CRN-SMOTE), which enhances oversampling via noise reduction using clustering techniques. The study is evaluated using multiple performance metrics across four datasets, showing promising results compared to established methods such as RN-SMOTE, SMOTE-Tomek Link, and SMOTE-ENN.

Strengths:

The topic is timely and addresses a significant gap in machine learning classification, particularly with imbalanced datasets.The problem of classifier bias toward majority classes is well-articulated, providing a clear motivation for the study. The integration of clustering for noise reduction within SMOTE is a novel approach, offering theoretical and practical contributions. The study utilizes multiple datasets (ILPD, QSAR, Blood, and Maternal Health Risk) and evaluates performance with a comprehensive set of metrics (Cohen's kappa, MCC, F1-score, precision, and recall). Consistent performance improvements across datasets demonstrate robustness and applicability. The results indicate a statistically significant improvement over state-of-the-art methods, which is well-supported by comparative data. The abstract and introduction effectively contextualize the study, making the research accessible to both academic and applied audiences. By advancing oversampling techniques, the manuscript contributes to improved classification performance for minority classes, particularly in critical domains such as health risk assessment. The implications extend beyond the datasets studied, offering potential benefits across various imbalanced datasets. Addressing class imbalance remains a critical research area in machine learning, with applications spanning healthcare, finance, and beyond. This manuscript aligns well with current trends and offers solutions that practitioners and researchers can readily adopt.

Conclusion

The manuscript provides a significant advancement in addressing imbalanced data classification. The proposed CRN-SMOTE method is well-motivated, thoroughly evaluated, and impactful, with the potential to influence both research and practice. The authors have demonstrated rigorous methodology, solid experimental design, and clear presentation of results. Therefore, I strongly recommend accepting the manuscript for publication

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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 #2: No

Reviewer #3: Yes: Richard Govada Joshua

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