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Imbalanced Data Sets Classification Based on SVM for Sand-Dust Storm Warning
Joint Authors
Xie, Yonghua
Liu, Yurong
Fu, Qingqiu
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-01
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
In view of the SVM classification for the imbalanced sand-dust storm data sets, this paper proposes a hybrid self-adaptive sampling method named SRU-AIBSMOTE algorithm.
This method can adaptively adjust neighboring selection strategy based on the internal distribution of sample sets.
It produces virtual minority class instances through randomized interpolation in the spherical space which consists of minority class instances and their neighbors.
The random undersampling is also applied to undersample the majority class instances for removal of redundant data in the sample sets.
The comparative experimental results on the real data sets from Yanchi and Tongxin districts in Ningxia of China show that the SRU-AIBSMOTE method can obtain better classification performance than some traditional classification methods.
American Psychological Association (APA)
Xie, Yonghua& Liu, Yurong& Fu, Qingqiu. 2015. Imbalanced Data Sets Classification Based on SVM for Sand-Dust Storm Warning. Discrete Dynamics in Nature and Society،Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1060672
Modern Language Association (MLA)
Xie, Yonghua…[et al.]. Imbalanced Data Sets Classification Based on SVM for Sand-Dust Storm Warning. Discrete Dynamics in Nature and Society No. 2015 (2015), pp.1-8.
https://search.emarefa.net/detail/BIM-1060672
American Medical Association (AMA)
Xie, Yonghua& Liu, Yurong& Fu, Qingqiu. Imbalanced Data Sets Classification Based on SVM for Sand-Dust Storm Warning. Discrete Dynamics in Nature and Society. 2015. Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1060672
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1060672