The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Neighbourhood-based undersampling approach for handling imbalanced and overlapped data
Open accessAbstract
Abstract Class imbalanced datasets are common across different domains including health, security, banking and others. A typical supervised learning algorithm tends to be biased towards the majority class when dealing with imbalanced datasets. The learning task becomes more challenging when there is also an overlap of instances from different classes. In this paper, we propose an undersampling framework for handling class imbalance in binary datasets by removing potential overlapped data points. Our methods are designed to identify and eliminate majority class instances from the overlapping region. Accurate identification and elimination of these instances maximise the visibility of the minority class instances and at the same time minimises excessive elimination of data, which reduces information loss. Four methods based on neighbourhood searching with different criteria to identify potential overlapped instances are proposed in this paper. Extensive experiments using simulated and real-world datasets were carried out. Results show comparable performance with state-of-the-art methods across different common metrics with exceptional and statistically significant improvements in sensitivity.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359
ADASYN: Adaptive synthetic sampling approach for imbalanced learning
He, H., Bai, Y., Garcia, E. A., & Li, S. (2008) · 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) · doi:10.1109/ijcnn.2008.4633969 · Cited by 4,521
Borderline-SMOTE : A New Over-Sampling Method in Imbalanced Data Sets Learning
Han, H., Wang, W.-Y., & Mao, B.-H. (2005) · Lecture notes in computer science · doi:10.1007/11538059_91 · Cited by 4,331
The class imbalance problem: A systematic study1
Japkowicz, N., & Stephen, S. (2002) · Intelligent Data Analysis · doi:10.3233/ida-2002-6504 · Cited by 3,295
Learning from class-imbalanced data: Review of methods and applications
Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., & Bing, G. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.12.035 · Cited by 2,377
Asymptotic Properties of Nearest Neighbor Rules Using Edited Data
Wilson, D. L. (1972) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1972.4309137 · Cited by 2,130
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance
Seiffert, C., Khoshgoftaar, T. M., Van Hulse, J., & Napolitano, A. (2010) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/tsmca.2009.2029559 · Cited by 1,867
In Defense of One-Vs-All Classification
Rifkin, R. M., & Junior, A. B. D. R. K. (2004) · Cited by 1,598
López, V., Fernández, A., García, S., Palade, V., & Herrera, F. (2013) · Information Sciences · doi:10.1016/j.ins.2013.07.007 · Cited by 1,590
Cited by
Parkinson’s Disease: Bridging Gaps, Building Biomarkers, and Reimagining Clinical Translation
Tanaka, M. (2025) · doi:10.20944/preprints202506.2099.v2 · Cited by 57
Artificial Intelligence to Improve Antibiotic Prescribing: A Systematic Review
Amin, D., Garzόn-Orjuela, N., Garcia Pereira, A., Parveen, S., Vornhagen, H., & Vellinga, A. (2023) · Antibiotics · doi:10.3390/antibiotics12081293 · Cited by 34
Metadata
- Authors
- Vuttipittayamongkol, P., & Elyan, E.
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 509
- Pages
- 47-70
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W2971749073
- Cited by
- 286
- References
- 44
- FWCI
- 12.43
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Neighbourhood-based undersampling approach for handling imbalanced and overlapped data. https://app.cochat.ai/papers/10.1016/j.ins.2019.08.062
- The paper
- Vuttipittayamongkol, P., & Elyan, E. (2020). Neighbourhood-based undersampling approach for handling imbalanced and overlapped data. Information Sciences. https://doi.org/10.1016/j.ins.2019.08.062
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
