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FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning.

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...

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Publication status:
Published

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Publisher copy:
10.1109/TFUZZ.2010.2042721

Authors


Batuwita, R More by this author
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Institution:
University of Oxford
Department:
Oxford, MPLS, Computer Science
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

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