Output details
11 - Computer Science and Informatics
Coventry University
Article title
FSVM-CIL: Fuzzy support vector machines for class imbalance learning
Type
D - Journal article
Title of journal
IEEE Transactions on Fuzzy Systems
Article number
-
Volume number
18
Issue number
3
First page of article
558
ISSN of journal
1063-6706
Year of publication
2010
URL
-
Number of additional authors
1
Additional information
<24> The paper presents a method of improving SVMs for class imbalance learning (called Fuzzy-SVM-CIL), to successfully handle the class imbalance in the presence of outliers and noise. It was the first paper, when published, addressing the problem of class imbalance for FSVMs. The proposed FSVM-CIL method has been thoroughly validated on several real-world imbalanced datasets. The technique presented in the paper has influenced and is a component part in the development of many recent methods on class imbalance for SVMs, as evident from citations. The work has been funded by a prestigious Clarendon PhD scholarship award at Oxford University.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
36
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
