The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Classification of Imbalanced Data by Using the SMOTE Algorithm and Locally Linear Embedding
Abstract
The classification of imbalanced data is a common practice in the context of medical imaging intelligence. The synthetic minority oversampling technique (SMOTE) is a powerful approach to tackling the operational problem. This paper presents a novel approach to improving the conventional SMOTE algorithm by incorporating the locally linear embedding algorithm (LLE). The LLE algorithm is first applied to map the high-dimensional data into a low-dimensional space, where the input data is more separable, and thus can be oversampled by SMOTE. Then the synthetic data points generated by SMOTE are mapped back to the original input space as well through the LLE. Experimental results demonstrate that the underlying approach attains a performance superior to that of the traditional SMOTE
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
Cited by
Amin, A., Anwar, S., Adnan, A., Nawaz, M., Howard, N., Qadir, J., Hawalah, A., & Hussain, A. (2016) · IEEE Access · doi:10.1109/access.2016.2619719 · Cited by 310
Classifying earthquake damage to buildings using machine learning
Mangalathu, S., Sun, H., Nweke, C. C., Yi, Z., & Burton, H. V. (2020) · Earthquake Spectra · doi:10.1177/8755293019878137 · Cited by 298
Prediction of $$\beta $$-Thalassemia carriers using complete blood count features
Rustam, F., Ashraf, I., Jabbar, S., Tutusaus, K., Mazas, C., Barrera, A. E. P., & de la Torre Diez, I. (2022) · Scientific Reports · doi:10.1038/s41598-022-22011-8 · Cited by 28
Metadata
- Authors
- Wang, J., Xu, M., Wang, H., & Zhang, J.
- Venue
- 2006 8th international Conference on Signal Processing
- Publisher
- IEEE
- Published
- 2006
- OpenAlex
- W2150088099
- Cited by
- 197
- References
- 1
- FWCI
- 0.48
- Type
- conference paper
- Language
- en
- Open access
- closed
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: Classification of Imbalanced Data by Using the SMOTE Algorithm and Locally Linear Embedding. https://app.cochat.ai/papers/10.1109/icosp.2006.345752
- The paper
- Wang, J., Xu, M., Wang, H., & Zhang, J. (2006). Classification of Imbalanced Data by Using the SMOTE Algorithm and Locally Linear Embedding. 2006 8th international Conference on Signal Processing. https://doi.org/10.1109/icosp.2006.345752
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.
