New Fuzzy Support Vector Machine for the Class Imbalance Problem in Medical Datasets Classification
Joint Authors
Wang, Hongyuan
Gu, Xiaoqing
Ni, Tongguang
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-03-23
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
In medical datasets classification, support vector machine (SVM) is considered to be one of the most successful methods.
However, most of the real-world medical datasets usually contain some outliers/noise and data often have class imbalance problems.
In this paper, a fuzzy support machine (FSVM) for the class imbalance problem (called FSVM-CIP) is presented, which can be seen as a modified class of FSVM by extending manifold regularization and assigning two misclassification costs for two classes.
The proposed FSVM-CIP can be used to handle the class imbalance problem in the presence of outliers/noise, and enhance the locality maximum margin.
Five real-world medical datasets, breast, heart, hepatitis, BUPA liver, and pima diabetes, from the UCI medical database are employed to illustrate the method presented in this paper.
Experimental results on these datasets show the outperformed or comparable effectiveness of FSVM-CIP.
American Psychological Association (APA)
Gu, Xiaoqing& Ni, Tongguang& Wang, Hongyuan. 2014. New Fuzzy Support Vector Machine for the Class Imbalance Problem in Medical Datasets Classification. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1050016
Modern Language Association (MLA)
Gu, Xiaoqing…[et al.]. New Fuzzy Support Vector Machine for the Class Imbalance Problem in Medical Datasets Classification. The Scientific World Journal No. 2014 (2014), pp.1-12.
https://search.emarefa.net/detail/BIM-1050016
American Medical Association (AMA)
Gu, Xiaoqing& Ni, Tongguang& Wang, Hongyuan. New Fuzzy Support Vector Machine for the Class Imbalance Problem in Medical Datasets Classification. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1050016
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1050016