The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Borderline over-sampling for imbalanced data classification
Open accessAbstract
Traditional classification algorithms usually provide poor accuracy on the prediction of the minority class of imbalanced data sets. This paper proposes a new method for dealing with imbalanced data sets by over-sampling the borderline minority class instances. A Support Vector Machine (SVM) classifier is then trained to predict future instances. Compared with other over-sampling methods, the proposed method focuses only on the minority class instances residing along the decision boundary, due to the fact that this region is the most crucial for establishing the decision boundary. Furthermore, the artificial minority instances are generated in such a way that the regions of the minority class with fewer majority class instances would be expanded by extrapolation, otherwise the current boundary of the minority class would be consolidated by interpolation. Experimental results show that the proposed method achieves a better performance than other over-sampling methods.
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Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
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
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,156
A Tutorial on Support Vector Machines for Pattern Recognition
Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,429
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359
Ensemble methods in machine learning
Dietterich, T. G. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45014-9_1 · Cited by 8,408
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
A study of the behavior of several methods for balancing machine learning training data
Batista, G. E. A. P. A., Prati, R. C., & Monard, M. C. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007735 · Cited by 4,203
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
Cited by
A comprehensive survey on support vector machine classification: Applications, challenges and trends
Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.10.118 · Cited by 2,381
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
Lemaître, G., Nogueira, F., & Aridas, C. K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.06570 · Cited by 1,514
A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data
Khushi, M., Shaukat, K., Alam, T. M., Hameed, I. A., Uddin, S., Luo, S., Yang, X., & Reyes, M. C. (2021) · IEEE Access · doi:10.1109/access.2021.3102399 · Cited by 335
COVID-19 cough classification using machine learning and global smartphone recordings
Pahar, M., Klopper, M., Warren, R., & Niesler, T. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104572 · Cited by 290
A Cost-Sensitive Deep Belief Network for Imbalanced Classification
Zhang, C., Tan, K. C., Li, H., & Hong, G. S. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2832648 · Cited by 282
Metadata
- Authors
- Nguyen, H. M., Cooper, E. W., & Kamei, K.
- Venue
- International Journal of Knowledge Engineering and Soft Data Paradigms
- Publisher
- Inderscience Publishers
- Published
- 2011
- Volume
- 3
- Issue
- 1
- Pages
- 4
- ISSN
- 1755-3210 (ISSN-L)
- 1755-3229
- OpenAlex
- W2168508521
- Cited by
- 592
- References
- 32
- FWCI
- 3.55
- Type
- journal article
- Language
- en
- Open access
- green
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- Nguyen, H. M., Cooper, E. W., & Kamei, K. (2011). Borderline over-sampling for imbalanced data classification. International Journal of Knowledge Engineering and Soft Data Paradigms. https://doi.org/10.1504/ijkesdp.2011.039875
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