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Selecting the Optimal Combination Model of FSSVM for the Imbalance Datasets
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-03-16
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
Imbalanced data learning is one of the most active and important fields in machine learning research.
The existing class imbalance learning methods can make Support Vector Machines (SVMs) less sensitive to class imbalance; they still suffer from the disturbance of outliers and noise present in the datasets.
A kind of Fuzzy Smooth Support Vector Machines (FSSVMs) are proposed based on the Smooth Support Vector Machine (SSVM) of O.
L.
Mangasarian.
SSVM can be computed by the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm or the Newton-Armijo algorithm easily.
Two kinds of fuzzy memberships and three smooth functions can be chosen in the algorithms.
The fuzzy memberships consider the contribution rate of each sample to the optimal separating hyperplane.
The polynomial smooth functions can make the optimization problem more accurate at the inflection point.
Those changes play the active effects on trials.
The results of the experiments show that the FSSVMs can gain the better accuracy and the shorter time than the SSVMs and some of the other methods.
American Psychological Association (APA)
Qin, Chuandong& Zhao, Huixia. 2014. Selecting the Optimal Combination Model of FSSVM for the Imbalance Datasets. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-479806
Modern Language Association (MLA)
Qin, Chuandong& Zhao, Huixia. Selecting the Optimal Combination Model of FSSVM for the Imbalance Datasets. Mathematical Problems in Engineering No. 2014 (2014), pp.1-6.
https://search.emarefa.net/detail/BIM-479806
American Medical Association (AMA)
Qin, Chuandong& Zhao, Huixia. Selecting the Optimal Combination Model of FSSVM for the Imbalance Datasets. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-479806
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-479806