- Abstract:
-
Support vector machines (SVMs) is a popular machine learning technique, which works effectively with balanced datasets. However, when it comes to imbalanced datasets, SVMs produce suboptimal classification models. On the other hand, the SVM algorithm is sensitive to outliers and noise present in the datasets. Therefore, although the existing class imbalance learning (CIL) methods can make SVMs less sensitive to class imbalance, they can still suffer from the problem of outliers and noise. Fuz...
Expand abstract - Publication status:
- Published
- Publisher copy:
- 10.1109/TFUZZ.2010.2042721
-
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- Journal:
- IEEE T. Fuzzy Systems
- Volume:
- 18
- Issue:
- 3
- Pages:
- 558-571
- Publication date:
- 2010
- DOI:
- EISSN:
-
1941-0034
- ISSN:
-
1063-6706
- URN:
-
uuid:23631618-7878-458f-a0c1-7c98227bf253
- Source identifiers:
-
299317
- Local pid:
- pubs:299317
- Language:
- English
- Keywords:
- Copyright date:
- 2010
Journal article
FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning.
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