Imbalanced Data Sets Classification Based on SVM for Sand-Dust Storm Warning

Imbalanced Data Sets Classification Based on SVM for Sand-Dust Storm Warning

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 vir...

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Main Authors: Yonghua Xie, Yurong Liu, Qingqiu Fu
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Discrete Dynamics in Nature and Society
Online Access:http://dx.doi.org/10.1155/2015/562724
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spelling doaj-f0aaf1f274d94d199d74294bb95c5d6a2020-11-25T00:15:23ZengHindawi LimitedDiscrete Dynamics in Nature and Society1026-02261607-887X2015-01-01201510.1155/2015/562724562724Imbalanced Data Sets Classification Based on SVM for Sand-Dust Storm WarningYonghua Xie0Yurong Liu1Qingqiu Fu2School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaDepartment of Mathematics, Yangzhou University, Yangzhou 225002, ChinaSchool of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaIn 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.http://dx.doi.org/10.1155/2015/562724
collection DOAJ
language English
format Article
sources DOAJ
author Yonghua Xie
Yurong Liu
Qingqiu Fu
spellingShingle Yonghua Xie
Yurong Liu
Qingqiu Fu
Imbalanced Data Sets Classification Based on SVM for Sand-Dust Storm Warning
Discrete Dynamics in Nature and Society
author_facet Yonghua Xie
Yurong Liu
Qingqiu Fu
author_sort Yonghua Xie
title Imbalanced Data Sets Classification Based on SVM for Sand-Dust Storm Warning
title_short Imbalanced Data Sets Classification Based on SVM for Sand-Dust Storm Warning
title_full Imbalanced Data Sets Classification Based on SVM for Sand-Dust Storm Warning
title_fullStr Imbalanced Data Sets Classification Based on SVM for Sand-Dust Storm Warning
title_full_unstemmed Imbalanced Data Sets Classification Based on SVM for Sand-Dust Storm Warning
title_sort imbalanced data sets classification based on svm for sand-dust storm warning
publisher Hindawi Limited
series Discrete Dynamics in Nature and Society
issn 1026-0226
1607-887X
publishDate 2015-01-01
description 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.
url http://dx.doi.org/10.1155/2015/562724
work_keys_str_mv AT yonghuaxie imbalanceddatasetsclassificationbasedonsvmforsandduststormwarning
AT yurongliu imbalanceddatasetsclassificationbasedonsvmforsandduststormwarning
AT qingqiufu imbalanceddatasetsclassificationbasedonsvmforsandduststormwarning
_version_ 1725387110662275072

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